Technical Addendum to the
Winningsplan Groningen 2013
Subsidence, Induced Earthquakes and
Seismic Hazard Analysis in the Groningen
Field

Date:

November 2013

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Contents
0

Executive Summary ................................................................................................................................... 10
0.1

Background ................................................................................................................................. 10

0.2

Introduction ................................................................................................................................. 10

0.3

Historical Seismicity in the Groningen field ..................................................................................... 10

0.4

Results of Seismological Studies based on the historical seismic record .............................................. 11

0.5

Assessment of the Hazard ............................................................................................................. 11

0.6

Uncertainty in the Hazard Assessment ............................................................................................ 11

0.7

Alternative Production Strategies ................................................................................................... 12

0.7.1

Depletion .................................................................................................................................... 12

0.7.2

Pressure Maintenance .................................................................................................................. 13

0.8

Hazard Analysis of Alternative Production Strategies ....................................................................... 14

0.9

Results of Geomechanical Studies .................................................................................................. 14

0.10

Studies Programme ...................................................................................................................... 15

0.11

Independent Review ..................................................................................................................... 17

0.12

Independent review of ‘Report to the Technical Guidance Committee (TBO) on Subsurface Aspects
of Induced Earthquakes in the Groningen Field’ (part 1) .................................................................. 17

0.13

Independent review of ‘Report to the Technical Guidance Committee (TBO) on alternative
production measures and hazard analysis’ (part 2) ......................................................................... 18

1

Introduction .............................................................................................................................................. 21

2

Introduction to the Geology and Seismicity in the Groningen field .................................................................. 23

3

2.1

Regional Geology and Conceptual Structural Model ........................................................................ 23

2.1.1

Structural setting .......................................................................................................................... 23

2.1.2

Stratigraphy and depositional setting ............................................................................................. 25

2.1.3

Reservoir model and rock properties .............................................................................................. 27

2.2

Current Monitoring Network and Historical Seismicity in the Groningen Field .................................... 29

2.2.1

Previous Work ............................................................................................................................. 29

2.2.2

Seismic Monitoring Network ......................................................................................................... 29

2.3

Earthquake Statistics..................................................................................................................... 30

2.4

Compaction and Seismicity ........................................................................................................... 32

2.5

References................................................................................................................................... 34

Gas production from the Groningen field..................................................................................................... 35
3.1

Historical production .................................................................................................................... 35

3.2

Pressure development in the field over time ..................................................................................... 37

3.3

Development projects ................................................................................................................... 39

3.3.1

Compression ............................................................................................................................... 39

3.3.2

New development wells ................................................................................................................ 39

3.3.3

Development of the North-Western periphery ................................................................................. 40

3.3.4

Development of the South-Western periphery ................................................................................. 40

3.3.5

Development of gas from the Carboniferous below the Groningen field ............................................. 41

3.4

Depletion of the Groningen Field ................................................................................................... 41

3.4.1

Depletion behaviour under the current production philosophy .......................................................... 41

3.4.2

Depletion behaviour under an alternative production philosophy ...................................................... 43

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3.4.3

Effect of subsurface uncertainty on depletion behaviour ................................................................... 46

3.5

Pressure Maintenance .................................................................................................................. 48

3.6

Scenarios .................................................................................................................................... 48

3.7

Conclusion .................................................................................................................................. 51

Historical compaction / subsidence and future trends ................................................................................... 52
4.1

Summary .................................................................................................................................... 52

4.2

Introduction ................................................................................................................................. 52

4.3

Geodetic and geomechanical data ................................................................................................ 52

4.3.1

Survey protocol............................................................................................................................ 52

4.3.2

Surveying techniques .................................................................................................................... 53

4.3.3

Survey design .............................................................................................................................. 54

4.3.4

Levelling and Insar data used for the calibration ............................................................................. 55

4.3.5

Data from uniaxial compaction experiments ................................................................................... 56

4.4

The Groningen Geomechanical model ........................................................................................... 58

4.4.1

Linear and Bi-linear Compaction Models ........................................................................................ 60

4.4.2

Time Decay Compaction Model ..................................................................................................... 62

4.4.3

Linear Isotach Compaction model .................................................................................................. 63

4.4.4

Upscaling of input data ................................................................................................................ 64

4.5

Comparing the finite element model with Geertsma and van Opstal, the possible influence of rock
salt creep .................................................................................................................................... 65

4.5.1

Zechstein salt creep ...................................................................................................................... 65

4.6

Calibration of the model ............................................................................................................... 69

4.6.1

RMS of all benchmarks using epoch combinations ........................................................................... 69

4.6.2

Basecase model ........................................................................................................................... 70

4.6.3

Results for the Bi-linear model ....................................................................................................... 71

4.6.4

Time decay.................................................................................................................................. 73

4.6.5

Isotach model .............................................................................................................................. 75

4.6.6

Comparison ................................................................................................................................ 78

4.6.7

Conclusion .................................................................................................................................. 79

4.7

Subsidence forecasts .................................................................................................................... 79

4.8

References................................................................................................................................... 82

Illustrative examples of induced seismicity from other locations ....................................................................... 84
5.1

Introduction ................................................................................................................................. 84

5.1.1

Gazli .......................................................................................................................................... 84

5.1.2

Lorca .......................................................................................................................................... 85

5.1.3

Rotenburg ................................................................................................................................... 85

5.1.4

Lacq ........................................................................................................................................... 85

5.1.5

Yibal .......................................................................................................................................... 85

5.1.6

Ekofisk ........................................................................................................................................ 85

5.1.7

Fashing ....................................................................................................................................... 85

5.1.8

Other hydrocarbon fields in the Netherlands .................................................................................. 85

5.2

Concluding remarks ..................................................................................................................... 86

5.3

References................................................................................................................................... 86

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5.4
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Tables ......................................................................................................................................... 87

Release of energy by Earthquakes ............................................................................................................... 89
6.1

Analysis of the induced earthquake record ..................................................................................... 89

6.1.1

Frequency-magnitude distribution .................................................................................................. 89

6.1.2

Physical limits on the maximum magnitude ..................................................................................... 92

6.1.3

Finite strain limit .......................................................................................................................... 92

6.1.4

Finite fault size limit...................................................................................................................... 92

6.1.5

Finite mass limit ........................................................................................................................... 93

6.2

The earthquake strain model ......................................................................................................... 93

6.2.1

Stress, compaction, faults and earthquakes ..................................................................................... 94

6.2.2

Earthquakes and gas production ................................................................................................... 96

6.2.3

Earthquakes and compaction ........................................................................................................ 98

6.2.4

Seismological model .................................................................................................................... 99

6.3

Potential influence of Time-Dependent and Rate-dependent Effects on future Seismicity ..................... 101

6.3.1

Time-dependent effects ............................................................................................................... 102

6.3.2

Rate-dependent effects................................................................................................................ 102

6.4

Conclusions ............................................................................................................................... 103

6.5

Recommendations for further work............................................................................................... 103

6.6

References................................................................................................................................. 104

Movement at surface ............................................................................................................................... 106
7.1

Ground-Motion Prediction Equations (GMPEs) .............................................................................. 106

7.2

Development of GMPEs for Groningen ......................................................................................... 108

7.2.1

Criteria for Groningen GMPEs .................................................................................................... 108

7.2.2

The Groningen Ground-Motion Database .................................................................................... 109

7.2.3

Selection and Modification of the Akkar et al. (2013) GMPE .......................................................... 112

7.2.4

Epistemic Uncertainty in Ground-Motion Predictions ...................................................................... 114

7.2.5

Spatial Correlations in Ground Motions ....................................................................................... 114

7.2.6

Duration of Ground Shaking ....................................................................................................... 115

7.3

Conclusions ............................................................................................................................... 115

7.4

References................................................................................................................................. 116

Probabilistic Seismic Hazard Assessment ................................................................................................... 118
8.1

Interpretation of Eurocode 8 for the case of production-induced seismicity ....................................... 118

8.2

Hazard assessment: summary of approach and method implemented ............................................. 118

8.3

Hazard maps: PGA and PGV for a 475 year return period ............................................................ 119

8.4

Sensitivity tests ........................................................................................................................... 122

8.5

Assumptions and limitations ........................................................................................................ 123

8.6

Time evolution of seismic hazard: projections based on planned production .................................... 124

8.7

PGA hazard assessment ............................................................................................................. 124

8.8

Logic-tree to assess epistemic uncertainties ................................................................................... 127

8.8.1

Classification of epistemic uncertainties ........................................................................................ 127

8.8.2

Topology of the logic-tree ........................................................................................................... 129

8.8.3

Elements of the logic-tree ............................................................................................................ 130

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8.8.4

Results of the logic-tree ............................................................................................................... 135

8.9

Seismic hazard assessments for different pressure depletion scenarios ............................................. 136

8.9.1

Seismological assessments for each scenario ................................................................................ 136

8.9.2

Ground motion assessments for each scenario .............................................................................. 140

8.10

Hazard Summary ...................................................................................................................... 142

8.11

Conclusions ............................................................................................................................... 143

8.12

Recommendations for further work............................................................................................... 144

8.13

References................................................................................................................................. 144

The Role of Faults .................................................................................................................................... 147
9.1

Introduction ............................................................................................................................... 147

9.2

Seismicity in a geomechanical evaluation framework..................................................................... 147

9.2.1

Stored energy ............................................................................................................................ 148

9.2.2

Failure ...................................................................................................................................... 149

9.2.3

Release of stored energy ............................................................................................................. 149

9.2.4

The role of faults and the release of stored energy in the Groningen field ........................................ 150

9.3

What we know and what we don’t know (yet) .............................................................................. 151

9.3.1

Equilibrium of forces (stress), either statically or dynamically .......................................................... 151

9.3.2

Subsurface structure ................................................................................................................... 152

9.3.3

Material behavior ...................................................................................................................... 152

9.3.4

Loading conditions, initial and production-induced ........................................................................ 153

9.3.5

Monitoring activities and further investigations .............................................................................. 156

9.4

Geomechanical modeling ........................................................................................................... 158

9.4.1

Full 3D modeling ....................................................................................................................... 158

9.4.2

2-Dimensional modeling of fault slip ............................................................................................ 159

9.5

2D Geomechanical investigation of the potential causes of fault slip and seismic events .................... 160

9.5.1

Model features........................................................................................................................... 161

9.5.2

In-situ stress and formation properties .......................................................................................... 163

9.5.3

Model results ............................................................................................................................. 164

9.5.4

Summary of findings 2D modeling ............................................................................................... 169

9.5.5

Next steps 2D modeling ............................................................................................................. 170

9.6

Three-dimensional modelling results ............................................................................................. 172

9.6.1

Geomechanical assessment of seismicity with full field geomechanical model (URC) .......................... 172

9.6.2

Geomechanical assessment of seismicity with full field geomechanical model ................................... 179

9.7

Conclusions ............................................................................................................................... 183

9.8

References................................................................................................................................. 184

Reducing the Remaining Uncertainty in the Hazard..................................................................................... 185
10.1

Introduction ............................................................................................................................... 185

10.2

Data acquisition activities ........................................................................................................... 185

10.2.1 Introduction ............................................................................................................................... 185
10.2.2 Extension passive seismic network ............................................................................................... 185
10.2.3 Subsurface vertical seismic arrays ................................................................................................ 186
10.2.4 Temporary vertical seismic arrays ................................................................................................ 187

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10.2.5 Permanent vertical seismic arrays ................................................................................................ 187
10.3

The 2014 Study and Data Acquisition program on Induced earthquakes in the Groningen field ........ 187

10.3.1 Geomechanical discussion to reduce uncertainty in the partitioning factor ....................................... 188
10.3.2 Continuation of the Study Program .............................................................................................. 190
10.4
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References................................................................................................................................. 192

Appendix A: Full 3D geomechanical modeling methodology ....................................................................... 193
11.1

Introduction ............................................................................................................................... 193

11.2

Geological model input .............................................................................................................. 193

11.3

Material properties .................................................................................................................... 194

11.4

Initial stress state ........................................................................................................................ 197

11.5

Global model analysis methodology ............................................................................................ 198

11.6

Sub-model analysis methodology ................................................................................................ 203

Appendix B: Pressure Maintenance ........................................................................................................... 209
12.1

Introduction ............................................................................................................................... 209

12.2

References:................................................................................................................................ 209

12.3

Selection of the injection medium ................................................................................................. 209

12.4

Nitrogen (N2) ............................................................................................................................ 210

12.5

Carbon dioxide (CO2) ................................................................................................................ 211

12.6

Flue Gas and Air ....................................................................................................................... 211

12.7

Conclusion ................................................................................................................................ 211

12.8

Gas recovery with nitrogen injection. ........................................................................................... 212

12.9

Introduction ............................................................................................................................... 212

12.10

Objectives of the subsurface study ............................................................................................... 213

12.11

Basis for modelling..................................................................................................................... 213

12.12

Simulation workflow ................................................................................................................... 216

12.12.1 Test model ................................................................................................................................. 216
12.12.2 Full Field Model (FFM) ................................................................................................................ 218
12.13

Results and observations ............................................................................................................. 220

12.14

Alternative Development Options ................................................................................................. 223

12.14.1 Partial Voidage Replacement ...................................................................................................... 223
12.14.2 Injection in the northern part of the field only ................................................................................ 224
12.14.3 Different start up times for the injection ......................................................................................... 227
12.14.4 Summary of the development options. .......................................................................................... 227
12.15

Development plan for pressure maintenance using nitrogen injection .............................................. 228

12.16

Technical premises ..................................................................................................................... 228

12.17

Non-technical premises .............................................................................................................. 228

12.18

Assumptions .............................................................................................................................. 228

12.19

Development concept ................................................................................................................. 229

12.19.1 Plot lay-out ................................................................................................................................ 229
12.19.2 Injection wells ............................................................................................................................ 230
12.19.3 Air Separation Units (ASU) ......................................................................................................... 231
12.19.4 Nitrogen Rejection Unit (NRU) ..................................................................................................... 232

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12.19.5 Pipeline infrastructure ................................................................................................................. 233
12.19.6 Project Execution planning .......................................................................................................... 234
12.19.7 Operations and maintenance ...................................................................................................... 234
12.19.8 High level development cost estimate (screening level) ................................................................... 234

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12.20

Project uncertainty...................................................................................................................... 235

12.21

Project optimisation .................................................................................................................... 236

12.22

Environmental and societal impact ............................................................................................... 236

12.23

Gas recovery with water injection ................................................................................................ 237

12.24

Conclusions ............................................................................................................................... 238

12.25

References................................................................................................................................. 239

Appendix C: Conclusions / Insights .......................................................................................................... 240
13.1

Maximum possible magnitude ..................................................................................................... 240

13.2

Probabilistic seismological assessment .......................................................................................... 241

13.3

Probabilistic seismic hazard assessment ....................................................................................... 241

13.4

Remaining technical uncertainties ................................................................................................ 242

13.5

Future data gathering designed to reduce uncertainties .................................................................. 242

13.6

References................................................................................................................................. 243

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0 Executive Summary
0.1

Background

This report provides technical support for the update of the Groningen Winningsplan of 1st December 2013
and contains details of the analysis of subsidence and earthquakes induced by gas production from the
Groningen field. This report presents the significant study progress on:





Results of geomechanical and seismological studies into induced earthquakes in the Groningen field
based on the historical earthquake record with forecasts of future seismicity
Results of the analysis of compaction of the reservoir rock and subsidence with forecasts of future
subsidence
Impact of different depletion scenarios and the application of pressure maintenance on the seismic
hazard

In a letter to Parliament (11th February 2013 reference TK 2012-2013 33529, no. 2), the Minister of
Economic Affairs initiated a number of studies on induced earthquakes in the Groningen area. Study
number 5 addresses “research into the maximum strength of earthquakes associated with the Groningen
gas field”. Study number 6 addresses “research into different production methods to reduce the number
and magnitude of earthquakes”. A Technische Begeleidingscommissie Ondergrond (TBO; Technical
Guidance Committee Sub-surface) and Steering Committee were set up by the Minister of Economic Affairs
to oversee this study work and ensure quality and independence. The results of these studies 5 and 6 have
been summarised in this report.

0.2

Introduction

Although considerable progress was made in the understanding of the seismic hazard, uncertainty remains
at present. Note that the uncertainty range around the seismic hazard (and hence predictions around the
maximum) is not constant but instead grows in the future (even in the next 5 to 10 years). This increasing
uncertainty, however, is intended to be balanced by an overall programme to further calibrate and validate
assumptions and to take appropriate preventive measures, where acceptable limits may be exceeded.
The scientific knowledge and uncertainty range described in this report reflects the current level of
understanding, but not necessarily the true physical uncertainty. Consequently, predictions of the seismic
hazard range are believed to be conservative. To reduce the uncertainty NAM has initiated a further data
acquisition programme to obtain additional field data, as well as a studies programme. Once the
uncertainty is reduced, the conservatism and related preventive measures may be scaled down accordingly.
The execution of the field data acquisition plan and the integration of data in the studies program will be
secured in the monitoring protocol as addendum to the Winningsplan (the “Beheersprotocol voor Borging
van het Seismisch Risico binnen grenzen”, (Ref. 1)). This protocol and associated monitoringsplan contain a
proposal on how to validate, update and report the risk assessment and related key assumptions on an
annual basis. This is expected to result in a continuous improvement and more constrained assessment of
the seismic hazard over the coming years.

0.3

Historical Seismicity in the Groningen field

Since the early nineties, relatively small earthquakes have occurred in the vicinity of the Groningen gas
field. It is recognized that these events are induced by the production of gas from the field. In 1993, a
geophone monitoring network, operated by KNMI, was installed in the field. This was extended several
times and has a detection limit of ML=1.5 since 1996. Several cases of earthquakes induced by gas
production have been recorded in literature. A possible analogue for larger earthquakes in the Groningen
area is the 2004 earthquake of magnitude ML =4.4 in the Rotenburg gas field (Germany), which also
produces from the Rotliegend.
The earthquake in the Huizinge area on the August 16th, 2012 was the strongest event recorded to date in
the Groningen Province with magnitude ML =3.6 and caused damage to buildings and raised significant
public concerns and subsequent feelings of fear.

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Following this event the minister of EZ initiated an extensive study program to better understand the
occurrence and magnitude of the earthquakes induced by the production of gas from the field and to
assess the hazard these impose. NAM’s study and data acquisition plan at that stage was merged into this
program. Early 2013, the Minister of Economic Affairs installed the Technische Begeleidingscommissie
Ondergrond (Technical Guidance Committee Sub-Surface) and the Steering Committee to oversee, advise
on and assure these studies. The results and insights gained with the sub-surface studies are presented by
NAM in two reports, which have been issued to the TBO.

0.4

Results of Seismological Studies based on the historical seismic record

The Groningen gas field is one of the largest gas fields in the world and has been producing since late
1963. Currently, the gas is produced through 20 processing locations (clusters), consisting of 8 to 12 wells
each, gas treatment facilities and compressors. The production from the field causes the reservoir pressure
to decline. This results in compaction of the reservoir rock, which leads to subsidence at surface and strain
build-up in the reservoir rock. It is believed that accommodation of (a fraction of) this compaction strain by
seismogenic fault slip is the source of the observed seismicity. If this fraction were invariant with production,
the maximum magnitude of a future event is expected to be ML =4.5 with a 95% upper bound of ML =5.5.
However, the fraction of induced strain accommodated by earthquakes is likely to be increasing with ongoing reservoir compaction and this explains the observed distribution of earthquakes in time and over the
Groningen area. Table 0.1 shows the modeling results based on a statistical analysis of the current
earthquake ‘catalogue’, combined with first-order geomechanical assumptions, i.e. de-emphasizing the
role of individual faults in the reservoir.
Period

Table 0.1:

0.5

Maximum Magnitude

Maximum PGA

P50

P10

P2

P50

P10

P2

2013 - 2016

3.4

4.1

4.6

0.02g

0.12g

0.30g

2013 - 2018

3.6

4.4

4.9

0.03g

0.18g

0.42g

2013 - 2023

3.9

4.8

5.3

0.06g

0.33g

0.67g

Summary of the maximum magnitude and maximum PGA values with 50%, 10% and 2% chances of
exceedance over three different time periods of assessment according to the current production plan, the
time-decay compaction model and the modified Akkar et al. (2013) GMPE.

Assessment of the Hazard

To characterise the seismic hazard in a manner that is relevant to the potential impact of the earthquake on
the built environment, it is necessary to quantify the hazard in terms of the nature of the ground shaking
produced at any given surface location. The simplest and most widely-used of these parameters is the
maximum amplitude on the acceleration time series, the peak ground acceleration (PGA). Using collections
of ground-motion recordings, empirical equations have been developed, relating PGA to variables like the
magnitude and the distance between the earthquake and the site of recording. These relationships are
generally called ground-motion prediction equations, or GMPEs. Existing appropriate GMPEs derived from
recordings of tectonic earthquakes in Europe and the Middle East, adjusted to provide a good fit to the
Groningen data at smaller magnitude levels, were adopted for the prediction of PGA.
A Monte-Carlo approach to Probabilistic Seismic Hazard Assessment (PSHA) was identified as being best
suited to the analysis of the Groningen field’s induced seismicity. Guided by Eurocode 8 interpreted for
non-stationary seismicity, maps of ground motion equivalent to the 475 year return period have been
calculated.

0.6

Uncertainty in the Hazard Assessment

The most prominent uncertainty affecting the calculated hazard is the large uncertainty in the coupling
coefficient relating seismic moment budget to the volumetric strain due to compaction (the partitioning
factor).

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Also, the choice of model relating reservoir pressure decline to compaction introduces significant
uncertainty in the longer term. The uncertainty range in the compaction was estimated by using three
different compaction models. These were the bi-linear model, the time-decay model and the linear isotach
model. For the bi-linear and time-decay models a reduced rock compressibility was used, while for the
isotach model both the rock compressibility measured on core material and the reduced compressibility
were used to obtain a fit with historically measured subsidence data.
Sensitivity tests showed that the selection of a suitable ground motion prediction equation (GMPE) and the
future production period chosen for calculating the annualised hazard had a considerable influence on the
resulting hazard estimates. Comparing these results with those for tectonic earthquakes is problematic, also
because tectonic earthquakes typically have a longer duration and occur at greater depth.
More detailed geomechanical modeling with an emphasis on the role of faults is in progress, and utilizes
explicit modeling of faults within a multi-scale geomechanical modeling framework. This work has the
potential to physically constrain the partitioning factor, thereby reducing uncertainty of model predictions.
Currently, applications of all geomechanical models are hampered by the paucity of relevant field data,
leaving room for multiple geomechanical explanations. A higher resolution of more field parameters is
required to make a step forward in a better understanding of geomechanical behavior. A further data
acquisition campaign is in progress, including logging of geomechanical rock properties and installation of
geophones in two deep observation wells. The data obtained with the geophones in the deep wells will be
shared as stipulated in the borgingsprotocol (Ref. 1). Additionally, the existing monitoring network of
shallow geophones and accelerometers will be extended.
Advances in geomechanical modeling combined with the new field data will be used to improve the
understanding of the fault slip behavior and when validated by the observed seismicity, this has the
potential to reduce the currently large uncertainties. Explicit geomechanical / fault movement modeling is
showing insightful results. This should lead to a more robust geomechanical workflow for use in seismic
hazard analysis to enable subsurface stress management as a tool to reduce the hazard. An important
example are the ongoing geomechanical studies, that explicitly model faults in the reservoir, of which it is
expected that it will be able to demonstrate an absolute upper bound to the maximum magnitude.

0.7

Alternative Production Strategies

0.7.1

Depletion

The Groningen field is currently produced by a depletion mechanism. Effort is made to maintain, given the
production level, the pressure differences in the main area of the field as small as possible. Several
measures have been investigated that could potentially reduce induced seismicity:



An alternative production philosophy is to preferentially produce the southern part of the field, which
(up till now) is less earthquake prone, in an attempt to keep the reservoir pressure in the area around
Loppersum relatively high. This would reduce compaction around Loppersum, which potentially has a
temporary beneficial impact on local seismicity. As a negative consequence the pressure difference
between northern and southern part of the field will increase.



Reduction of the production level of the field is another potential measure to reduce seismicity. Lower
production levels will lead to longer production times to produce the reserves and will therefore reduce
the number of earthquakes over a time period (e.g. during a year), but not necessarily the total number
of earthquakes during the production life of the field.



In the western periphery of the field, the reservoir pressure in a number of poorly drained blocks
remains higher than in the main area of the field. Depletion of these blocks to reduce the pressure
difference with the main area of the field has the potential to reduce seismicity.



Maintaining the reservoir pressure by injecting fluid/gas volumes to balance the gas withdrawal also
has the potential to reduce seismicity. This requires extensive and costly redevelopment of the field,
including the construction of large new facilities on a substantial industrial scale, which will have a
large impact on the surroundings (Ref. 3).

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To investigate the impact of the different measures on the seismicity and the hazard, production forecasts
and the corresponding reservoir pressure development for different production scenarios have been
prepared. For these different production scenarios the resulting compaction (contour maps and associated
volumes) was estimated as input into the seismic hazard analysis. This analysis showed that the difference
in compaction, resulting from the different depletion scenarios over the period 2013 – 2023 is smaller than
the spread from using the different compaction models. For the different production scenarios the
incremental compaction volume over this 10 year period ranges from 50 to 58 mln m 3 using the time
decay model. However, for the base case production scenario the incremental compaction volume over
this 10 year period ranges from 58 to 76 mln m3 for different compaction models. This illustrates that the
uncertainty introduced by the different compaction models is larger than the spread introduced by the
different production scenarios.
0.7.2

Pressure Maintenance

Currently, the Groningen field is planned to be produced under a depletion drive mechanism. Low
abandonment pressures are required to reach the highest ultimate gas recovery from the field, which is part
of effective recovery (as included as a principle in the Dutch mining regulations). Consequently, this also
leads to highest compaction, subsidence and the resulting seismic activity. Maintaining the reservoir
pressure in the Groningen field at a level above the abandonment pressure will, after a certain delay
period, halt depletion-induced compaction and subsidence. Therefore, it has the potential to reduce
induced seismicity in the field.
A study was carried out to establish the feasibility and contours of pressure maintenance projects in the
context of Groningen, and to assess the impact on seismicity. Initially, different injection media were
screened (nitrogen carbon dioxide, flue gas, water). Based on its abundant presence, relatively inert
behaviour and tolerance for presence in the sales gas, nitrogen was chosen as the optimal injection
medium to conduct a screening study. Use was made of the reservoir model of the Groningen field, the
experience with gas-gas displacement and gas-mixing from the Grijpskerk and Norg underground gas
storage projects, and the nitrogen injection project in De Wijk.
This study aims to provide a first:




outline of the project scope, and
assessment of the effectiveness to reduce the frequency and magnitude of earthquakes.

Reservoir processes for nitrogen injection are more complex and fundamentally different from the current
depletion drive process. Breakthrough of nitrogen in producer wells is expected to materially reduce
Groningen gas reserves. The large electricity consumption required for the production of nitrogen further
reduces the gas volume available for sales.
The scope of the project is beyond current global experience (estimated to be 5 – 7 times larger than the
largest similar nitrogen injection project ever installed). Although the scope and dimensions of a pressure
maintenance project have been defined, feasibility of the project has not yet been established.
Nitrogen injection takes at least 5 years to first injection and some 10 years to ramp up to full capacity.
The total cost of the project is estimated to be between 6 and 10 bln €. The project will carry a material
environmental footprint, with extensive land use and electricity consumption. Societal support for a largescale nitrogen injection project is therefore considered a key determining factor. Alternative approaches
for reducing the magnitude and frequency of earthquakes will have to be assessed.
Results presented herein reflect an early stage assessment of the nitrogen injection gas recovery concept.
Scope has been identified for optimization in a number of areas.
Pressure maintenance is expected to arrest reservoir compaction, and hence to reduce the seismic hazard.
However, to what extent is yet to be established through further geomechanical modelling. Especially, the
impact on seismicity when injecting into or close to a fault needs to be addressed.

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0.8

Hazard Analysis of Alternative Production Strategies

Using a Monte Carlo method (NAM, 2013), the Akkar et al (2013) ground motion prediction equation
modified to be consistent with ground motion data from the Groningen Field and the earthquake record of
at least magnitude 1.5 events for Groningen, the following PSHA results were obtained for the base
production scenario:




For the 10 years from 2013 to 2023 based on the time-decay reservoir compaction model the PSHA
indicates a 50%, 10% and 2% probability of peak ground acceleration, PGA, exceeding 0.06g, 0.33g
and 0.67g respectively, where g is the standard acceleration of gravity (9.81 m/s2).
For a 5-year assessment period from 2013 to 2018 the corresponding results are a 50%, 10% and 2%
probability of PGA exceeding 0.03g, 0.18g and 0.42g respectively. Similarly for a three year
assessment period from 2013 to 2016, there is a 50%, 10% and 2% chance of PGA exceeding 0.02g,
0.12g and 0.30g respectively.

It is important to point out that the 2% over 10 years hazard results listed above should not be considered
equivalent to the 10% chance over 50 years stipulated by Eurocode 8, because induced seismicity within
the Groningen Field varies with time and is not modelled as a Poisson process.
Epistemic uncertainty characterises uncertainty in the values of parameters which are fixed but either
unknown or known only to within a certain precision. Significant epistemic uncertainties exist in the seismic
hazard assessment for the Groningen Field; these are primarily associated with strain partitioning, the
GMPE and reservoir compaction. A logic-tree approach, in which each branch of the logic-tree represents
a distinct scenario of a particular model and associated parameter values, was used to explore the impact
of the key epistemic uncertainties on the estimated hazard. It was found that the strain partitioning
uncertainty has the largest impact of those considered. The epistemic uncertainty attributed to strain
partitioning will in some part be due to random (aleatory) variability, representing the stochastic nature of
the earthquake process. The relative contribution of epistemic and aleatory uncertainties to the overall
uncertainty in strain partitioning is still unknown. Further earthquake, surface acceleration, and subsidence
monitoring within the Groningen Field in combination with additional geomechanical studies will provide
more information that may help to better constrain these uncertainties and therefore reduce conservatism in
the assessment of their consequences. .
Probabilistic seismic hazard assessments from 2013 to 2023 were conducted for 19 distinct scenarios that
form a representative sample of the different geological, compaction, development, and production
scenario elements. The results obtained indicate all these depletion scenarios yield similar seismic hazard
estimates with the exception of the emergency stop scenario, which reduces the maximum PGA with a 2%
chance of exceedance to 0.41g. This represents a 38% reduction compared to 0.67g for the current case
(SN STD).
In the emergency stop scenario, the linear isotach compaction model predicts significant rates of
compaction will persist until long after 2080. There is no analogue field evidence to support such
behaviour. Equally, laboratory measurements are unable to constrain the isotach model behaviour on the
multi-decadal time-scale exhibited here. For these reasons, the reliability of this particular isotach model
with a high compressibility coefficient is doubtful. Using a lower compressibility coefficient gives
compaction results comparable to the results of the time decay model and is considered to be a more
realistic representation.

0.9

Results of Geomechanical Studies

Geomechanical studies to increase our understanding of the physical fault slip process have also been
extended. A first series of analyses has been carried out aimed at identifying potential fault slip
mechanisms and the impact of various modelling options and their associated parameters on the simulated
geomechanical response. A two-dimensional modelling environment is chosen to allow a wide range of
modelling options and parameters to be evaluated relatively quickly.
Geomechanical models cannot directly predict radiated seismic energy; however, more detailed models
that explicitly capture fault slip behavior could provide an advanced methodology to better predict the

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evolution of seismic fault slip and related radiated energy. Advanced multi-scale mechanical modeling
combined with a geomechanics-seismicity relationship might provide a suitable framework to better
understand seismic fault slip behavior and assess future seismicity via a physically-constrained strain
partitioning (i.e., partition factor). This will be the subject of future study work, which will aim to establish a
workflow leading to a robust integration between 3D explicit fault modelling and the existing Seismological
Modeling to reduce seismic uncertainty. This work may help to constrain the uncertainty in the partitioning
factor.
Some relevant insights have been gained into the potential fault slip mechanisms that might be responsible
for the recorded seismic events in the Groningen field. Three different responses have been found based on
the formation offset along an existing fault plane, namely for down-thrown formations with an offset
smaller and larger than the thickness of the depleting reservoir and for up-thrown reservoir formations. The
difference in response is expressed in term of the depletion level at the onset of fault slip, the number of slip
sections, the total length of the slip sections, the maximum Relative Shear Displacement and the dissipated
energy by slip along the fault plane. The reported values for the mentioned response parameters are not
representative for the Groningen field conditions at this stage.
These new insights open the opportunity to look for seismic fingerprints to 1) determine if slip along existing
faults is indeed the root cause of the recorded seismic events, and 2) to discriminate between different slip
mechanisms, constrained by available structural information. Further studies are underway to evaluate the
impact of other modelling options.
The geomechanical / fault movement Finite Element Analysis (FEA) models may provide the capability to
reduce the uncertainty in partitioning factor calculations. A number of these models have shown good
agreement with field measurements and because they explicitly account for faults (a subset of faults
interpreted from seismic data was included in the model), this is a promising approach to provide a
physically meaningful basis for relating compaction strain energy with energy exerted during fault slip
(dissipated energy).
Results illustrate the potential advantages of this approach:





Temporal evolution of fault energy dissipation from geomechanical models correlates reasonably well
with observed radiated energy from seismic events
As a first step the partitioning or scaling factor that relates radiated energy with dissipated energy was
assumed to stay constant within the analyzed period (1996-2012).
Predicted energy dissipation is significantly different in different regions of the field in agreement with
observed seismicity

While the comparison is encouraging, further improvement may be established by enhancing the
representation in the FEA models of the faults, fault behaviors (e.g., slip / velocity weakening), compaction
physics (e.g. non-linear, rate-dependent compaction), etc. Also the full range of models consistent with the
historic seismicity data accounting for the stochastic variability due to finite sample effects within the
earthquake catalogue should be assessed. By providing a “physics-based” constraint on the relationship
between compaction strain energy and fault dissipated energy, these models could reduce the variability in
partitioning factors that had previously been calculated based on models without explicit consideration of
faults.

0.10

Studies Programme

The logic tree analysis showed that the uncertainty in the partitioning factor is the largest epistemic
contribution to the uncertainty in the hazard. Reducing this uncertainty is the prime objective of continued
data gathering in the field in combination with continued geomechanical studies. The geomechanical
modelling in combination with additional data from geophones in deep wells and near surface is expected
to be able to reduce the uncertainty in the partitioning factor and therefore also the uncertainty in the
hazard. A continuation of the study program is therefore planned. The main elements of the study and
data acquisition plan continuing in 2014 include: investigations to improve subsidence prediction,
extensive geomechanical modelling, monitoring of earthquakes in the Loppersum area with geophones

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located in deep wells, field-wide monitoring of earthquakes with geophones and ground accelerations with
accelerometers.
References:
1
Beheersprotocol voor Borging van het Seismisch Risico binnen grenzen, NAM, November 2013.

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0.11

Independent Review

The technical work presented in this report has been assured using internal assurance processes. The
Internal assurance is supplemented by independent (external) assurance. For the petroleum engineering
work SGS Horizon has been contracted to perform an independent review of the sub-surface models
(Geology and Reservoir Engineering).
The geomechanical and seismological studies have been reviewed by Professor of Seismology and Rock
Physics, Ian Main of the University of Edinburgh. Professor Main’s areas of expertise are: processes that
lead up to failure events, from earthquakes, rock fracture, and volcanic eruptions to failure of building
materials and bridges, and in quantifying the resulting hazard.
The study work documented in this report was presented to Prof Main in two sections. These correspond to
the two TBO report (studies 5 and 6). He prepared two separate reviews, which have been included in this
report.

0.12

Independent review of ‘Report to the Technical Guidance Committee (TBO) on Subsurface
Aspects of Induced Earthquakes in the Groningen Field’ (part 1)

Review by Ian Main, University of Edinburgh, Oct 7 2013.
Recommendations by the reviewer are given in italics below.
This is a comprehensive and very well written report which comes to some clear conclusions on (a)
characterising the current level of seismic hazard associated with future production of gas from the
Groningen field, and (b) making projections for future levels of hazard associated with the planned
production scenario, both using best practice as far as possible at this stage. All of the major conclusions
and caveats listed are reasonable in the light of current knowledge (see e.g. Ellsworth, 2013), as are the
plans in going forward with outstanding work to reduce the remaining uncertainties further.
As an external reviewer I was really impressed by the speed of assimilation of my previous suggestions into
the document in terms of new work additional work done, the effect these changes have already made in
the knock-on conclusions, and in conditioning the plans for future work, the most important of which are
the planned borehole passive seismic monitoring, the characterisation of the ground motion site response
and the full 3D geo-mechanical simulation characterisation and validation.
All of the authors and project managers should be commended for completing such a comprehensive and
professional piece of work to get to such a point in such a short time frame. I was also impressed by the
use of competing hypotheses, both in fitting current data and in making forecasts for future behaviour, in
addition to applying the formal error analysis provided.
The report squarely addresses the major question 5: “What is the largest induced earthquake that can be
expected in the Groningen area?”, but does this as part of a more comprehensive and appropriate
analysis of events of all sizes and probabilities. From this is clear that the main hazard is likely to come
from intermediate-sized events, and I agree with the authors that maximum magnitude itself is not the best
way to characterise the overall hazard. However, the analysis has not yet so far been coupled with an
analysis of vulnerability and hence it is not yet known what likely magnitude range constitutes the biggest
risk or impact to society.
Further work outside the scope of the current document will be needed to quantify and calibrate the site
response and its variability, and to assess the exposure, vulnerability and risk per se, for example along
the lines of the ‘Cat’ modelling community in the Insurance industry, using GIS data bases likely accessible
to the regulators. Perhaps this is better addressed in the report summarising the response to question 6,
although I note this question also focuses explicitly on hazard.

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It was good finally to see all of the various studies aimed at addressing question 5 brought together for the
first time, and especially the geo-mechanical modelling now made explicit in chapter 9. I also liked the
explicit section on ‘what we don’t know’ – always a good question. As acknowledged by the report:
The initial geo-mechanical model needs to be developed further, especially by more characterisation of the
constitutive rules for laboratory samples as well as field-scale or well tests and then to integrate the 3D
model into the seismic hazard analysis.
In addition I recommend in future work plotting the actual and geo-dynamically modelled stress state
positions relative to the strength envelope at the present time and the start of production, as well as any
other available snapshot times, to get an idea both of the current stress state and the trajectory for different
parts of the reservoir. It will be interesting to see if there is a correlation between the seismic to aseismic
partition coefficient and the proximity to the critical state curve along this stress trajectory, for example
illustrated as contours of the partition coefficient on the critical state diagram. Looking forward this may
provide a simple visual ‘dashboard’ for stress state monitoring and management.
While it is good to see finite size effects used as a constraint on maximum magnitude, the very high aspect
ratio of 60km by 300 m (200:1) is way above the average of 2:1 for rupture dimensions of small and
intermediate-magnitude events, though some large strike slip earthquakes that break the surface can have
larger ratios of ~10:1.
More work will be needed in future to determine the actual aspect ratio of the source ruptures, e.g. using
the aftershock area, and to determine the extent to which the overlying salt layer might resist the
propagation of dynamic slip. This is important because it will place constraints on the likelihood of surface
rupture. The new borehole seismic network will help greatly with this, in particular by reducing uncertainty
in source depth estimation.
Finally, it is clear from the report that understanding and constraining the partitioning coefficient between
seismic and total deformation and its evolution is still an issue. I have separately made some technical
suggestions on how to address this source of uncertainty as part of the on-going study, and note Shell have
already organised a much broader workshop on the issue.
Reference:
1

0.13

Ellsworth, W. (2013) Injection-induced earthquakes, Science 341, no. 6142, DOI:
10.1126/science.1225942)

Independent review of ‘Report to the Technical Guidance Committee (TBO) on alternative
production measures and hazard analysis’ (part 2)

Review by Ian Main, University of Edinburgh, 14 November 2013.
This report addresses the question of how the stress state in the subsurface may be managed to reduce the
hazard from future seismicity induced by gas production from the Groningen field. It is written at quite a
high level and is also quite dense, so it is not possible to check all of the details leading to a particular
conclusion. Nevertheless the conclusions are based on a large body of previous work, and there are many
significant new findings and recommendations. The report acknowledges many remaining sources of
uncertainty in calculating the future hazard, as well as summarising strategies to reduce the uncertainty in
terms of passive monitoring of the field and in better characterisation of the material properties in going
forward. Some of this uncertainty will reduce with additional data from the planned monitoring
programme, but a significant amount will remain.
The report explicitly considers the likely response of the reservoir to several potential scenarios aimed at
reducing the hazard by planned stress state management, including an ‘emergency stop’ and full pressure
support by injection of nitrogen as end-member strategies. The likely response of the reservoir and
associated hazard is complex and hard to predict exactly, as clearly acknowledged in the report. It may
depend on structural detail that cannot be resolved at present within the seismic resolution, or on model

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parameter uncertainty that can be addressed by more work on the materials concerned, conditioned on a
large programme of monitoring of the field, including new seismic data to be acquired from borehole sites.
One of the biggest sources of uncertainty is the strong effect of different models for compaction, which has
a greater effect than the differences in the different mitigation strategies.
The predicted hazard is expressed sensibly by a range of measures rather than a single number, enabling
risk management to be done with a sensible spread of possible outcomes. This is done by assuming a nonstationary Poisson process with a hazard rate that increases in time, conditioned on past behaviour. The
effect of different sources of uncertainty in hazard forecasting is estimated using a logic tree analysis of the
outcome of work conducted earlier in the programme. Overwhelmingly the main source of uncertainty that
emerges is the partition coefficient between seismic and aseismic (elastic and plastic) strain, and the degree
to which this may change in time. This is very sensibly identified as a priority in planning future work.
Two major new studies have been carried out using 2D and 3D geo-mechanical models, conditioned on
the structural and mechanical properties of the field, supported by a technical appendix.
This is a
necessary and impressive contribution to stress state management for hazard mitigation. The appendix
shows that a good body of field-specific data is available to condition the geo-mechanical models, though
more needs to be done as acknowledged, including better characterisation of materials outside the
reservoir horizon, and a study of the scaling properties of the relevant parameters. Despite this the models
already provide a good history match to past data, albeit likely with a large number of parameters that are
to some extent tuneable, and falsifiable forecasts of reservoir response are made for different model
parameters within the reasonable range. The forecasts will in time help discriminate between the
competing models as new data are collected.
The 2D modelling study predicts a maximum displacement within the reservoir of 0.5 m or so. This is
consistent with a maximum possible magnitude of around 6.5 in TBO report 5, based on the assumption
there of a 100% partition coefficient. Typically a magnitude 6 will produce 1m or so of slip, and the
modelled slip on a single fault in the present report is less than this. This may indicate the main hazard is
from smaller but more common events, consistent with the conclusion reported in TBO report addressing
question 5. By having such slip on many faults, and considering the potential for reactivated slip to
propagate or indeed nucleate in the surrounding layers this may resolve the problem of unrealistic aspect
ratios in the previous report (see also Table 6.1).
The report points out that even the ‘emergency stop’ scenario is not without risk – as demonstrated recently
in a real case in a geothermal energy context in Basel, Switzerland, showing that transient or timedependent effects do need to be taken into account, as considered explicitly here. Similarly maintaining
100% pressure in the reservoir may also require intrusive surface installations and workflows. In this case it
is important to acknowledge the possibility of hysteresis, i.e. recovering pressure fully will not be an
absolute guarantee of zero risk.
The executive summary presents a very good overview of the outcomes of the work programme so far,
given such a dense and technical report. It is also a fair summary of the state of knowledge at present and
presents sensible ways of moving forward, when taken in conjunction with the comprehensive list of
recommended further work in Table 6.1. This work in progress highlights the operational fact that risk
management will be a learning process, informed by feedback in the light of new data and the actual
outcomes.
My main general comments are as follows.
1.

It might have been useful to provide a more extended discussion of the rationale for the production
scenarios considered in the part 1 report. (The petroleum engineering aspects are considered
separately in part 2, but a brief summary of the broad rationale and explicit cross-reference to the
relevant sections might also be useful in part 1).

2.

All of the scenarios are long-term ‘smoothed’ scenarios for the population of induced events. It may
be useful also to examine and plan for adjustments to production as individual felt events occur, and

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to develop protocols in advance for hazard mitigation during such transients. Even at this stage it
seems quite plausible that a temporary ‘emergency stop’ might turn out to be sub-optimal.
3.

This and previous reports highlight a structural problem in the regulatory framework for seismic
hazard in Europe as a whole, not just in the Netherlands, i.e. its current basis in a stationary Poisson
model as a basis for probabilistic seismic hazard assessment. Here the hazard is both nonstationary and probably non-linear in time. As such the current report is very much placed at the
research frontier in terms of seismic hazard forecasting.

Non-stationary models are being developed in other contexts, and some new techniques may emerge from
the literature as time goes on (for natural as well as induced seismicity). For now the approach taken is
reasonable given the state of the art and the lack of clear guidelines on time-dependent hazard in Europe.
It is particularly strong both in identification and assessment of uncertainties, and in making specific plans
for reducing these. Much more work needs to be done of course, and there is likely an irreducible element
to the uncertainty which will need to be considered in risk management and communication, but many of
the key building blocks for stress state management to underpin this future effort are now in place.

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1 Introduction
This report provides technical support for the update of the Groningen Winningsplan of 1st December 2013
and contains additional details of the analysis of subsidence and earthquakes induced by gas production
from the Groningen field. This report presents study progress on:





Results of geomechanical and seismological studies into induced earthquakes in the Groningen field
based on the historical earthquake record with forecasts of future seismicity
Results of the analysis of compaction of the reservoir rock and subsidence with forecasts of future
subsidence
Impact of different depletion scenarios and the application of pressure maintenance on the seismic
hazard

In a letter to parliament (11 February 2013 reference TK 2012-2013 33529, no. 2), the Minister of
Economic Affairs initiated a number of studies on induced earthquakes in the Groningen area. Study
number 5 addresses “research into the maximum strength of earthquakes associated with the Groningen
gas field”. Study number 6 addresses “research into different production methods to reduce the number
and magnitude of earthquakes”. A Technische Begeleidingscommissie (TBO; Technical Guidance
Committee Sub-surface) and steering committee were set up to oversee this study work and ensure quality
and independence. The results of these studies have been incorporated in this report.
The research was carried out by experts from NAM, Shell Research Centre in Rijswijk and the ExxonMobil
Upstream Research Centre in Houston with support from external experts with relevant knowledge. The
report is contains 12 chapters (including this introductory chapter), which are briefly introduced below:
2.

3.

4.

5.

6.

7.

8.

9.

10.

This introduction to the document (1) is followed by an introduction into the geology of the
Groningen field. This section also contains historical background information relevant to induced
earthquakes, which will be referred to in later sections. The section concludes with a history of
induced seismicity in the Groningen field and a description of the current (2012) seismic network.
This section includes a discussion of the depletion philosophy for the Groningen field. An alternative
philosophy aiming to reduce earthquakes in the Loppersum area is introduced. The impact of
production curtailment and depletion philosophy on the reservoir pressure is considered.
Compaction of the reservoir rock in response to gas production plays a crucial role in the process of
inducing earthquakes. Section four explains the process and future trends expected in the
compaction and subsidence.
Induced earthquakes are not only experienced in the Groningen field, but in several other fields in
the world. Section five gives an overview of other fields, where earthquakes induced by gas
production earthquakes have been reported.
Section six on the release of energy by earthquakes is the core of this document. Starting with the
historical earthquake record, a statistical and geomechanical analysis is given of the frequency of
occurrence and magnitude of induced earthquakes and the expected future trend of the seismicity.
Once the earthquake has occurred, the released energy moves to the surface and causes movement
of the ground. This constitutes the hazard. Section seven describes this process and methods to
assess the ground movement based on the released energy.
In section eight, the insights into the induction of earthquake are combined with the method to assess
ground motions to provide hazard maps. It starts with a discussion of the hazard for limited time
periods (3, 5 and 10 years in the future) and an analysis of the uncertainty in the hazard analysis
using a logic tree approach. Based on compaction forecasts using different compaction models, the
seismic hazard for the current production plan and different production scenarios is estimated.
A review of the geomechanical studies into the role of faults is given in section nine. Geomechanical
modelling based on 2D fault geometries to gain fundamental insights into potential fault slip
mechanisms and of 3D full field and sub-models for regions of the field is described in this section of
the report.
Based on the current data the substantial uncertainties remain. Plans for additional data acquisition
activities to reduce the uncertainty in the earthquake parameters are laid out in section ten. A study

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11.
12.

13.

and data acquisition plan to reduce the remaining uncertainty in the hazard is summarized in this
section.
The methodology used in the 3D geomechanical modelling of the sub-models is summarized in this
appendix.
This appendix describes the option for pressure maintenance. In this selection of the injection
medium for pressure maintenance, several fluids are considered. Nitrogen is selected as the most
suitable option. The modelling of the displacement and mixing of the injected nitrogen and the
indigenous natural gas is described. Production forecasts and reserves estimates are prepared for a
base case development concept. The surface facilities required for a field development on the basis
of pressure maintenance through nitrogen injection is also sketched.
Conclusions and final Insights have been included in Section 13.

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2 Introduction to
Groningen field

the

Geology

and

2.1

Regional Geology and Conceptual Structural Model

2.1.1

Structural setting

Seismicity

in

the

The reservoir rocks of the Groningen field are dissected by a large number of natural faults. Some of these
natural faults are thought to play an important role in the creation of earthquakes. They represent zones of
weakness along which movement of volumes of reservoir rock can take place. Hence, it is key to
understand the distribution and character of the natural faults in the subject area. More detailed accounts
on the relation between earthquakes and natural faults are given elsewhere in this document. This section
describes the development of the fault system through time and its main geometrical characteristics.
The Groningen field is located on the Groningen High that has been a tectonically stable block since the
late Kimmerian uplift. The current depth of the Rotliegend reservoir, around 2600-3200 mTVD (true vertical
depth), reflects the maximum burial depth. The Groningen High is surrounded in the east by the Ems
Graben and Lower Saxony Basin and in the west by the Lauwerszee Trough (Figure 2.1). The Groningen
field is mainly fault-closed in all directions with dip closures present only locally. Top seal of the Groningen
field is provided by the overlying Zechstein salt. Figure 2.2 shows a seismic line from NW to SE illustrating
these features.

Figure 2.1

Main structural elements in the northern Netherlands

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Tertiary

Chalk
Triassic

Zechstein

Rotliegend
Carboniferous
Figure 2.2

Seismic cross-section through the Groningen field (see Figure 2.4 for detailed interpretation)

The Ems Graben and Lauwerzee Trough are results of a Late Carboniferous / Early Permian rifting phase
(extensional). The Groningen High was formed by the development of large North to West trending faults.
Initiation of these faults started prior to the Saalian erosional event which marks the Carboniferous-Permian
boundary across the region.
Most of these faults have been reactivated by two major tectonic events. The first was a period of SE-NW
extension which began during the Triassic and continued into the Late Jurassic. An inversion stage followed
during the late Jurassic-early Cretaceous (Kimmerian). Movement of the Zechstein salt also took place in
Cretaceous times. With the onset of the Alpine orogeny the area underwent a period of N-S compression,
lasting into the early Tertiary. This resulted in a second period of reactivation, evidenced in some areas by
transpressive skinny grabens and pop-up structures.
Detailed analysis of fault orientations, timing and kinematics has enabled to subdivide different structural
domains (SD1 to SD9 in Figure 2.3). Note that only a selection of the most important faults is included in
the structural model. Moreover, not all faults present in the subsurface are necessarily identified in the
seismic interpretation. SD1 in the South-Western periphery of the Groningen field is the most complex and
intensely faulted domain. It has experienced the most pronounced shortening and inversion, mainly along
NW- and E-trending faults, and manifested by pop-up structures.
SD5 in the Zeerijp area is another structurally complex domain. It is interpreted to be a depressed graben
feature bounded by two NNW-oriented oblique slip faults, which originated from a deep-seated older fault
zone. The difference in orientation between this fault zone and the Triassic-Jurassic stress field led to strikeslip movement along the graben boundary faults, and to rotation of fault blocks within the graben.
SD6 in the North-East is an example of a structurally quiet domain. E-W trending faults are branching from
a deeper-seated fault system, probably as a result of Jurassic extension and without evidence for later-stage
inversion.

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N

Figure 2.3

2.1.2

Structural domains in the Groningen field (total area ~ 40 x 40 km); faults shown are at Top_Rotliegend
level

Stratigraphy and depositional setting

A second important factor in the study of induced earthquakes, next to the structural model described
above, is the distribution of rock properties in the reservoir. For example: the higher its porosity, the higher
is the potential for the rock to compact as a result of gas production (the key role of compaction induced
seismicity is described in section 4). Porosity is high in sandstone rock and low in claystones. The
depositional processes that formed the sandstones are also important in the sense that rocks formed by
wind action (aeolian sediments) generally have higher porosities than those formed by river currents (fluvial
sediments). In the following, an overview is given of the important rock formations present in the subsurface
of Groningen.
The gas-bearing interval in the Groningen field comprises both Upper Rotliegend Group (Permian) and
Limburg Group (Carboniferous) sediments, separated by the Saalian Unconformity.
The Carboniferous sediments range in age from Westphalian-A to Westphalian-D. They represent lower to
middle delta plain deposition under humid, subtropical conditions. The Carboniferous strata have been
tilted and eroded during the Variscan orogeny (Figure 2.4). This created a depositional relief that was
gradually filled by the Rotliegend.

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SE

ZWD

SWO

UTB

PAU POS

SDM

ODP

USQ

NW

0

5

10km

0
in m SS

Tertiary

1000

Chalk & Lower Cretaceous
Jura

2000

Ten Boer

Zechstein

Trias

Slochteren

3000

Carboniferous

FWL

Figure 2.4

Schematic cross-section through the Groningen field indicating the main stratigraphic intervals. FWL
indicates the free water level.

The Upper Rotliegend Group comprises the Silverpit and Slochteren Formations. These are subdivided into
the following lithostratigrahic members (top to bottom): Ten Boer Claystone (ROCLT, heterolithic
shale/silt/sand), Upper Slochteren Sandstone (ROSLU, mainly sand), Ameland Claystone (ROCLA,
heterolithic shale/silt/sand) and the Lower Slochteren Sandstone (ROSLL, mainly sand).
Table 2.1

Lithostratigraphic Subdivision

Time

Group

Permian

Zechstein

Permian

Upper
Rotliegend

Carboniferous

Formation

Member

REGEO CODE
ZE

Silverpit

Ten Boer
Claystone

ROCLT

Slochteren

Upper Slochteren
Sandstone

ROSLU

Silverpit

Ameland
Claystone

ROCLA

Slochteren

Lower Slochteren
Sandstone

ROSLL

Limburg

DC

The Silverpit- and Slochteren Formations form a wedge of continental sediments that is onlapping
southwards onto the Carboniferous relief, and thickening towards the northwest (Figure 2.5). Rotliegend
sediments were deposited under arid, desert climate conditions. In the south of the Groningen area, along
the basin margin, the sediments consist of mixed alluvial/braided river deposits. These grade northwards
into mixed fluvial-aeolian deposits (Lower and Upper Slochteren Sandstone Members) and ultimately into
lake margin and desert lake fines (the Ten Boer and Ameland Claystone Members.

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SE

NW

Figure 2.5

Lithostratigraphic subdivision of the Rotliegend in the Groningen area

The Ameland and Ten Boer Claystone Members are characterized by silty to shaly wet sandflat deposits,
representing lake level highstands of regional scale as a response to climatic variations. The Ameland
Claystone can be clearly distinguished as a separate lithological unit in the northern half of the field, but
pinches out towards the South. Total thickness of Rotliegend sediments ranges from 140 m in the SSE to
300 m in NNW.
The Rotliegend is overlain by the sediments of the Zechstein interval. The Zechstein is dominated by
evaporate deposits, mainly halite, which forms a perfect seal for Rotliegend reservoirs. The lowermost part
of the Zechstein comprises a very competent and high-density carbonate/anhydrite interval which is
referred to as the Basal Zechstein. It is present throughout the greater Groningen area with a remarkably
constant thickness (~50 m in average). The large acoustic impedance contrast between the Basal Zechstein
and the overlying halite package creates a very strong seismic reflector, which is well-suited for mapping
the structural features described in section 2.1.1. The interval may also play an important role in the
transmission of seismic signals from a deeper source area to the surface. This is treated elsewhere in this
document.
2.1.3

Reservoir model and rock properties

The Rotliegend interval has been subdivided into the five Slochteren reservoir zones, four Slochteren
heterolithic zones, and three Ten Boer zones. A further vertical refinement was made by dividing the zones
into thinner layers. The resulting static framework consists of 12 reservoir zones, 203 layers and a total of
nearly 6 million grid cells. Approximately 90% of all grid cells have a thickness between 1 and 2 m.
Models to describe the distribution of reservoir properties in the Groningen field are based on the extensive
database of wireline log and core data. The modeling methodology applied is Sequential Gaussian
Simulation (SGS), supported by trend maps that reflect the regional depositional trends.
The mean Net-to-Gross for the Ten Boer Claystone ranges between 8 and 15%, but is typically larger than
95% for the Slochteren reservoir zones. The thin heterolithic zones separating the reservoir zones still have
mean NtG values as high as 75 - 80%. The distribution of porosity shows comparable trends, with mean
values of 6 – 10% for sandy interbeds in the Ten Boer Claystone, 15 - 18% for the reservoir zones, and
only slightly lower mean values for the sandy parts of the heterolithic intervals.
Best reservoir quality is seen in the central parts of the field where aeolian processes have played an
important role. Properties decrease towards the southern margin of the field, and towards the north. This is
in line with depositional trends of becoming more clay-rich towards the north, and more fluvial-dominated
or conglomeratic towards the south. as an example gives the porosity distribution of one of the reservoir
zones.
The mean permeability of the reservoir zones ranges from tens to hundreds of milliDarcies, gas saturation
ranges from 60 to 80%.

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Figure 2.6

Average porosity of one of the Upper Slochteren Sst reservoir zones

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2.2

Current Monitoring Network and Historical Seismicity in the Groningen Field

Since 1986 relatively small earthquakes have occurred in the vicinity of producing gas fields in the
northern Netherlands. These events occasionally caused feelings of unease amongst residents. Some of
these earthquakes in excess of a certain magnitude and intensity also caused damage to buildings, be it
mostly non-structural.
A multidisciplinary study, initiated by the Ministry of EZ and guided by a Supervisory Committee (BOA,
1993), analyzed the relationship between gas production and earthquakes, concluding that based on the
seismic pattern, the hypocenter locations, difference with historical seismicity and the frequency-magnitude
distribution, the earthquakes were of non-tectonic origin and most likely induced by reservoir depletion (i.e.
gas production). Following an agreement with the Royal Dutch Metereological Institute, KNMI, a borehole
seismometer network was installed in 1995 in Groningen to detect earthquakes, measure their epicenters
and quantify their magnitude. Accelerometers were also installed to measure ground motions in areas
where earthquakes frequently occurred.
The earthquake near Huizinge (16/8/2012) with magnitude ML=3.6 was experienced by witnesses as
more intense and with a longer duration of ground motion than previous earthquakes in the same area,
and certainly led to more emotions. Significantly more incidents of building damage were reported than for
previous earthquake events. Together with the general observation that seismicity in the Groningen area
seems to be increasing over the last years, and on instigation by SodM, the issue of gas production
induced seismicity in Groningen has become the subject of new studies and data acquisition.
2.2.1

Previous Work

After the BOA study, multiple studies were published on the mechanisms of induced seismicity (TNO, TUDelft) and statistics and analysis of recorded events (KNMI). Studies using numerical computer models
showed that pressure depletion resulting from gas production lead to a stress change on existing faults,
resulting in reactivation of these faults and consequently the occurrence of earthquakes. This is currently
accepted by both operators and governmental bodies as the mechanism that best explains the earthquakes.
Based on the recorded events, KNMI developed peak ground acceleration / peak ground velocity
(PGA/PGV) maps for a certain return period assuming a stationary model.
2.2.2

Seismic Monitoring Network

Prior to 1989, the lower detection limit for earthquakes was a magnitude of 2.5. Extension of the network
in Assen lowered this limit to 2.3, while the installation of a borehole seismometer station in Finsterwolde
further reduced the detection limit in the Groningen Field to 1.5 (De Crook et.al, 1998). In 1995, the KNMI
network was extended with 8 shallow boreholes all equipped with 3 component geophones at 4 levels (50
– 100 – 150 – 200 mTV depth). This monitoring network has been extended since;
1. in 2006, an accelerometer network with 8 accelerometers was installed,
2. in 2010 an additional 6 boreholes spread over the Northern Netherlands were added

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Figure 2.7

Left: an overview of the KNMI network, triangles indicate locations of the borehole stations; squares the
locations of the accelerometers. Right: Detections limits of earthquake magnitude (Dost et. al., 2012).

The detection limit of the present network is magnitude M=1, with a number of limitations described in Eck
et al (2004) and Dost et al (2012). The uncertainty in the location of the hypocenters is relatively large;
laterally 500m and vertically several kilometers. Additional to the borehole seismometers 12
accelerometers are in place. This network is being upgraded with replacement of 5 older accelerometers
and the installation of additional 6 accelerometers. This will improve measurement of lateral movement at
surface and the location of earthquake hypocenters. Further extension of the seismometer network is
planned for 2014, for details see section 10.2.3.

2.3

Earthquake Statistics

Around 220 earthquakes with magnitude ≥ 1.5 were registered in the period from 1991 till July 2013 in
the Groningen area. Before 1991, no earthquakes were reported from the Groningen area, but
earthquakes did occur in areas around Assen and Purmerend. It therefore seems plausible to assume that
no humanly perceptible events in the Groningen area occurred before 1991. Figure 2.8 shows the surface
projections (epicenters) of the recorded earthquakes, with in red the M=3.6 Huizinge event. More
information about this event can be found in Dost and Kraaijpoel (2013).

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200000

220000

240000

260000

Legend
Induced earthquakes
Magnitude
-0.8 - 1.4

600000

2.1 - 2.5
2.6 - 3.0

600000

1.5 - 2.0

3.1 - 3.5

580000

580000

3.6 - 4.0

0

10

20

Kilometres
200000

Figure 2.8

220000

240000

260000

Epicenters of the registered earthquakes by the KNMI (till July 2013)

The earthquake data published by KNMI are filtered using a threshold Ml ≥ 1.5 to avoid interference from
network changes. This threshold is valid since August 1995, making 1996 the first year with a complete
dataset. Figure 2.9 shows the yearly number of earthquakes categorized by magnitude intervals. From this
we conclude that on average the annual number of earthquakes has been increasing since 2003.
A comparison can also be made between gas production (variation) and the number of earthquakes. Due
to the relatively small amount of earthquakes with Ml ≥ 1.5 in Groningen, only correlations between annual
production (or production over a larger time interval) and earthquakes are relevant.
Figure 2.10 shows the relationship between the cumulative energy released by the earthquakes and the
amount of produced gas. The seismic energy can be calculated with the equation used by the KNMI (Van
Eck et al. 2004). It is based on the relation published by Ahorner & Pelzing (1985) between released
seismic energy (Es in Joule) and local magnitude (Ml):

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Figure 2.9

yearly amount of earthquakes since 1996, grouped by magnitude class

The production of gas leads to a decrease of the pressure in the reservoir, which is regarded to be the
largest driving force behind the earthquakes. The first earthquake (M l ≥ 1.5) was observed at a cumulative
production of around 1300 billion Nm3. A slight increase of the slope is visible at a production of around
1700 billion Nm3. This is further discussed in section 6.2.

Figure 2.10

2.4

Cumulative square root of seismic energy versus cumulative production of gas in the Groningen field

Compaction and Seismicity

An initial review of the location of highest seismicity with various reservoir parameters was carried out.
The distribution of earthquake epicenters was compared to incremental stress, bulk reservoir volume
change density (compaction) and fault density (Figure 2.11). The observed epicenters conformance is most
closely with the volume change density.

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Incremental stress

Volume change density

Fault density

Critical stress necessary but insufficient for observable seismicity
Observed earthquakes conform with reservoir volume changes
Fault reactivation is a likely mechanism1 - but which faults?
1 E.g. Lacq, Yibal, Ekofisk, Valhall, Fashing

Figure 2.11

Observed seismicity concentrates around the center of the field. This is also the area of largest volume
change density. Pressure depletion is largest in the south of the field away from this area of high
observed seismicity, while fault density is also high in areas with less observed seismicity.

Conformance between the observed seismicity and reservoir compaction is also apparent when the
observed earthquakes are plotted against the compaction at the epicenter and origin time of each
earthquake (Figure 2.12). Most earthquakes occurred after local compaction exceeded 18 cm (90%). The
same effect is apparent again by plotting maps of cumulative earthquake epicenters and the 18 cm
reservoir compaction contour as they develop through time (Figure 2.13). Seismicity is mostly observed
inside this compaction contour, while outside this contour, compaction appears to be almost aseismic.

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Figure 2.12

The observed earthquakes plotted for reservoir compaction against time. The size and color of the
marker indicate the magnitude of the earthquake.

Figure 2.13

The observed earthquakes plotted on the map of the field together with the 18 cm compaction contour
for four years (1991, 1997, 2003 and 2011).

2.5

References

Ahorner, L., Pelzing, R. (1985) The source characteristics of the Liège earthquake on November 8, 1983,
from digital recordings in West Germany. In: P. Melchior (ed), Seismic activity in Western Europe. D.
Reidel, Dordrecht, pp. 263-289.
BOA (1993) Eindrapport multidisciplinair onderzoek naar de relatie tussen Gaswinning en Aardbevingen
in Noord-Nederland; Begeleidingscommissie Onderzoek Aardbevingen, 1993
De Crook, Th., H.W. Haak & B. Dost (1998). Seismich risico in Noord-Nederland.KNMI (Koninklijk
Nederlands Meteorologisch Instituut), Techn. Rep., De Bilt, Netherlands.
Dost, B. and Kraaijpoel, D. (2012) The August 16, 2012 earthquake near Huizinge (Groningen). KNMI
report, KNMI De Bilt, 2013.
Dost, B., Goutbeek, F., van Eck, T., Kraaijpoel, D. (2012) Monitoring induced seismicity in the North of the
Netherlands: status report 2010. WR 2012-03.
Van Eck, T., Goutbeek, F., Haak, H., Dost, B. (2004) Seismic hazard due to small shallow induced
earthquakes. WR 2004-01.

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3 Gas production from the Groningen field
3.1

Historical production

The Groningen field was discovered by well SLO-1 in 1959. After this discovery, a public-private
partnership - between the NAM, its shareholders, DSM (now EBN) and the Dutch government - was set up.
Its objective was, and is, to pursue a joint policy in order to explore and exploit the gas accumulations
within the boundaries of the Groningen concession to maximize economic reserves.
The partners in this venture had a sense of urgency in the development and marketing of the Groningen
gas, as it was estimated that there was only a limited window of opportunity for the selling of this resource.
It was generally thought that nuclear energy would replace fossil fuel within the near future.
The field was developed by several production clusters in the Southern part of the field. At the time, the full
extent of the field was not known and it was thought that the entire field could be produced through these
Southern clusters. However, additional appraisal drilling in the Northern part of the field proved that the
Groningen gas volumes were larger than previously assumed. Furthermore, pressure measurement in these
appraisal wells indicated that, as the Northern part was declining in pressure with a significant delay
(Figure 3.1), additional production clusters in the North were required. A detailed description of the
facilities in the Groningen field can be found in the Groningen winningsplan.
In the early seventies, rapid development of the field continued with several production clusters in the
Northern part of the field.

South
Centre
North

Figure 3.1

Phased development of Groningen field and historical pressures

After the first oil crisis in 1973, it became clear to the Dutch government that nuclear energy would not
replace fossil fuels and the rapid development of the Groningen field was not considered desirable any
longer. Therefore, the Small Field Policy was introduced. The Small Field Policy is intended to stimulate the
exploration and development of smaller gas reservoirs onshore and offshore in The Netherlands. Any gas
found can be offered to GasTerra, who has the obligation to purchase the gas at reasonable terms and
conditions. For oil and gas companies this means that small fields can be developed attractively, no
separate commercial development is required in case of small finds, and these fields can be operated
without disruptions due to fluctuations in gas demand.
The overcapacity of the Groningen field made the Small Field Policy possible; the Groningen field would be
the swing provider (‘sluitpost’) between small fields’ production and customer demand. This role demanded
good co-operation between NAM and GasTerra in order to ensure market demand and supply were
matched and that NAM made its investment on the Groningen field in an optimum manner. The success of
the Small Fields Policy is illustrated in Figure 3.2.

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Figure 3.2

Annual gas demand and relative gas production of the Groningen field and small gas fields (“kleine
velden”)

With the introduction of the Small Fields Policy, a production philosophy has been introduced that balances
the production from the field to keep pressure differences between the North and South of the field at low
levels. In practice, this means that the clusters in the North of the field, where the cluster density is lower
and therefore in-place volumes per cluster are larger, are produced preferentially. The clusters in the South
of the field are only produced when required (i.e. mostly in winter). Exceptions to this are the relatively
small peripheral blocks in the South-Western part of the field. In these blocks, the reservoir pressure lags
behind the pressure in the main area of the Groningen field. Dedicated production wells are planned in
these areas (see section 2.2.2).
At the end of the 1980’s, with the continuous production of the Groningen field, it became clear that
compression at production clusters was required in order to reduce the suction pressure at the wells.
In addition, the predictions at the time indicated that in the summer months the small fields’ gas production
would be larger than the market demand, even with Groningen at its minimum flow.
This resulted in a decision to delay the installation of compression on the Groningen clusters and to develop
three Under Ground Storages: two by NAM - Norg and Grijpskerk, and one by BP (currently TAQA) in
Alkmaar. These UGSs are considered part of the Groningen system and are an extension of the Groningen
main field. They were built with the following requirements: high production capacity (security of supply in
winter) and large volume shifting capacity (i.e. increased demand in summer). This is illustrated in Figure
3.3.
In recent years, the gas demand has increased whilst the contribution of the small gas fields is steadily
declining (also Figure 3.3). As a result, Groningen production has increased and the UGS fields have been
used more frequently. Therefore, in 2012 the decision was made to expand the Norg UGS. This will be
followed by the installation of 2nd stage compression later this decade. Future development plans for
meeting the gas demand in the Groningen field are described in section 2.2.2.

Productie profiel NAM operated Assets van 2010

Productie profiel NAM operated Assets van 2003
mln m3

UGS

Groningen

mln m3

Small Fields

Groningen

Small Fields

350

350
329

YTD NAM Operated

49,972

YTD Groningen

29,122

YTD S mall Fields

20,850

300

250

200

200

150

150

100

100

50

50

0

0

01/01/2003
07/01/2003
13/01/2003
19/01/2003
25/01/2003
31/01/2003
06/02/2003
12/02/2003
18/02/2003
24/02/2003
02/03/2003
08/03/2003
14/03/2003
20/03/2003
26/03/2003
01/04/2003
07/04/2003
13/04/2003
19/04/2003
25/04/2003
01/05/2003
07/05/2003
13/05/2003
19/05/2003
25/05/2003
31/05/2003
06/06/2003
12/06/2003
18/06/2003
24/06/2003
30/06/2003
06/07/2003
12/07/2003
18/07/2003
24/07/2003
30/07/2003
05/08/2003
11/08/2003
17/08/2003
23/08/2003
29/08/2003
04/09/2003
10/09/2003
16/09/2003
22/09/2003
28/09/2003
04/10/2003
10/10/2003
16/10/2003
22/10/2003
28/10/2003
03/11/2003
09/11/2003
15/11/2003
21/11/2003
27/11/2003
03/12/2003
09/12/2003
15/12/2003
21/12/2003
27/12/2003

250

YTD UGS

3,096

YTD NAM Operated

60,142

YTD Groningen

46,850

YTD S mall Fields

13,292

01/01/2010
07/01/2010
13/01/2010
19/01/2010
25/01/2010
31/01/2010
06/02/2010
12/02/2010
18/02/2010
24/02/2010
02/03/2010
08/03/2010
14/03/2010
20/03/2010
26/03/2010
01/04/2010
07/04/2010
13/04/2010
19/04/2010
25/04/2010
01/05/2010
07/05/2010
13/05/2010
19/05/2010
25/05/2010
31/05/2010
06/06/2010
12/06/2010
18/06/2010
24/06/2010
30/06/2010
06/07/2010
12/07/2010
18/07/2010
24/07/2010
30/07/2010
05/08/2010
11/08/2010
17/08/2010
23/08/2010
29/08/2010
04/09/2010
10/09/2010
16/09/2010
22/09/2010
28/09/2010
04/10/2010
10/10/2010
16/10/2010
22/10/2010
28/10/2010
03/11/2010
09/11/2010
15/11/2010
21/11/2010
27/11/2010
03/12/2010
09/12/2010
15/12/2010
21/12/2010
27/12/2010

YTD UGS

300

UGS

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Figure 3.3

3.2

Intra-year production of NAM, 2003 versus 2010. (Small gas fields in green, Groningen main field in
blue and the UGSs in yellow)

Pressure development in the field over time

The current production philosophy aims to balance the production from the field and keeps pressure
differences low between the North and South. In some peripheral parts of the field, where the reservoir
pressure is lagging behind, there are opportunities for further development (see section 2.2.2 for more
detail). Figure 3.4 shows that the measured pressure lag in the gas in North West is relatively small (i.e.
maximum of some 20 bar). The measured pressure lag in the gas in the South West is larger (i.e. close to
100 bar). Note: The Harkstede block has been put in production through the Eemskanaal-13 well and is
preferentially produced as a result of the implemented production philosophy.

ZND
ZRP

observation
wells in SW

TBR

ZVN

pressure equilibration
philosophy
(i.e. equilibrate
pressure)

Figure 3.4

observation
wells in NW

Pressure difference in the field (in bar)

Due to declining reservoir pressure and subsequently a reduction in production capacity, the current
production philosophy will become more difficult to maintain. In other words: in order to meet demand, all
production locations in the field need to produce more frequently. As a result it is expected that the
pressure difference between North and South under the current production scenario will start to increase
again to some 20-25 bar. Figure 3.5 shows the forecasted pressure decline based on current estimated
market demand. Two production clusters are shown, ’t Zandt (ZND) in the North and Zuiderveen (ZVN) in
the South. The pressure in ZVN clearly shows the pressure response of preferential production during the
winter. These fluctuations, of as much as 10 bar, cannot be seen at Zeerijp where a steady and constant
yearly pressure decline is observed and forecasted (i.e. a pressure pulse originating from the South of the
field has no noticeable pressure effect in the North of the field). The annual pressure depletion of the field in
the period 2005-2015 and 2010-2020 is slightly less than 5 bar/year based on the imposed maximum
production cap of 425 bcm per decade (ignoring yearly fluctuations). This translates to 1 bar pressure
depletion for every 8 to 9 bcm produced.

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Highest pressure
in North minus
lowest in South

Due to
changing
demand
and
declining
capacity

Figure 3.5

North-South Pressure differential (left pane) and forecasted pressure decline (right pane) based on
current estimated market demand. (pink = Zuiderveen, blue = ‘t Zandt, Brown = Zeerijp, red = Ten Boer)

The reservoir model (Section 3.2) which is used for forecasting, is calibrated with more than 50 years of
production and pressure data. The model was history matched against more than 1700 down hole
pressure measurements. A good quality history match has been obtained with a relative small root-meansquare error of only 1.77 bar. Figure 3.6 illustrates the quality of the match by showing four production
clusters in different parts of the field as an example.

ZND (NORTH)

POS (WEST)

OLD1 (NW-Periphery)

ZPD (EAST)

SLO (SOUTH)
Figure 3.6

TBR4 (SW-Periphery)

KHM-1 (SW-Periphery

Historical production/pressure measurement (markers) versus simulated pressure (lines) for four
production clusters. Pressure is shown in Bar.

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3.3

Development projects

With the increasing level of pressure depletion in the Groningen field, it becomes increasingly more
difficult to provide the contractually agreed capacity and gas volumes. A number of projects have been
initiated (Figure 3.7) in order to mitigate the decline in production capacity. These projects can be put
into two different categories; installation of additional compression capacity and drilling new
development wells.
3.3.1

Compression

With continued depletion and reduction of reservoir pressure, additional compression stages will need to
be applied together with inter-stage cooling in order to reduce the tubing head pressure (THP) at the
wellhead and subsequently the pressure in the reservoir (Pres). Note that each bar of pressure depletion
relates to about 8 to 9 bcm of additional recovery for the Groningen field.
At present the first stage compressors in the field are being modified for lower suction pressure and lower
production rates. Also currently, the technical concept for second stage compression is being tested on
the Schaapbulten cluster. If technically successful, the concept will be applied to most of the other clusters.
A modeling study on the optimal phasing and implementation of second stage, and also later third stage
compression will be completed early 2014. This will determine at which clusters, and at what point in
time, installation of additional compression stages will be most optimal.
55.00

Modification to 1st stage
Schaapbulten Cluster
2nd stage
3rd stage
NW Pheriphery
SW Pheriphery

50.00

45.00

Ongoing-2014
Ongoing-2013
2017-2024
2025-2034
2018-2019
2016-2017

Decrease Pres to ~35 bar
Proof of technical concept
Decrease Pres to ~23 bar
Decrease Pres to ~13 bar
Drill and hook-up 5 to 7 wells
Drill and hook-up 3 to 5 wells

40.00

35.00

BCM per year

SW Pheriphery

NW Pheriphery

30.00

3rd stage
25.00

2nd stage
Schaapbulten
Cluster
Modification to
1st stage

20.00

15.00

10.00

5.00

2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050

0.00

Figure 3.7

3.3.2

Production forecast for the main development projects (as used for 2012 reserves submission). Stages
refer to compression stages.

New development wells

Based on the results of a major model refinement and update in 2012, an inventory has been made of
remaining infill opportunities in the Groningen field. These opportunities are mainly located in the
peripheral parts of the field where the reservoir pressure is known or expected to be lagging behind with
respect to the central parts.

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3.3.3

Development of the North-Western periphery

This is the area of the existing observation wells Barnheem-1, Stedum-1 and Zeerijp-1 (see Figure 3.8).
The expected additional recovery in the area is estimated around 5 bcm of gas. The notional
development plan for this area consists of the following elements:






Construction of a production pipeline to an existing cluster (either Leermens, ’t Zandt or Ten Post).
Drilling additional wells.
Modification and extension of existing installations.
Potential construction of a new drilling site and production satellite.

Figure 3.8

3.3.4

Notional lay-out of NW development project.Notional future wells indicated by red (surface)and blue
(reservoir level) dots. Black lines indicate the gas evacuation route to the Leermens cluster.

Development of the South-Western periphery

This is the area of the existing production clusters Eemskanaal, Kooipolder and Froombosch, and the
observation wells Kolham-1, Harkstede-2A and Ten Boer-4 (see Figure 3.9). The expected additional
recovery in the area is about 4 bcm. The development plan for this area include the following elements:






Drilling wells from the existing Ten Boer and Kolham locations.
Construction of a new drilling location, including a pipeline to a nearby production cluster.
Drilling of new wells from this new location.
Drilling of additional wells from the existing production clusters (Eemskanaal, Kooipolder,
Froombosch).

Figure 3.9

Notional lay-out of SW development project

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3.3.5

Development of gas from the Carboniferous below the Groningen field

Recent studies have demonstrated that the development of gas-bearing Carboniferous sandstone
reservoirs faces serious technical and economic challenges, for instance drilling through the already
depleted Rotliegend formation into potentially undepleted Carboniferous sands. Its feasibility will depend
on the availability of new drilling and completion technologies in the future. Experiences in the coming
years from Carboniferous fields in other parts of the country may lead to new insights.

3.4

Depletion of the Groningen Field

This section of the report described the current depletion plan for the Groningen field and introduces an
alternative depletion philosophy. Apart from production to satisfy market demand, curtailment is
considered. In a separate report (Ref. 1) production based on a pressure maintenance philosophy is
described. Based on the different development options and off take policies a number of scenarios are
presented. In section 3 the compaction volumes for these scenarios will be assessed and in section 4 the
seismic hazard.
3.4.1

Depletion behaviour under the current production philosophy

Since the introduction of the Small Fields Policy in the seventies, a production philosophy has been
introduced that balances the production from the Groningen field to minimise the pressure differences
between the North and South of the field. In practice, this means that the clusters in the North of the field,
where the cluster density is lower and therefore in-place volumes per cluster are larger, are produced
preferentially. The clusters in the South of the field are only produced when required (i.e. mostly in winter).
The effectiveness of the introduced production philosophy is shown in Figure 3.10, by the narrow pressure
band-width of some 10-25 bar for the majority of pressure measurements, after 1,000 Bcm was produced.
Exceptions to this are the relatively small peripheral blocks in the South-Western peripheral part of the
field.

Peripheral wells

GIIP South~1/3

Figure 3.10

GIIP North ~2/3

The P/Z plot for the Groningen field shows he decline in reservoir pressure with decreasing production.

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In Figure 3.11 the results of several production curtailment scenarios and the impact on the Groningen 2 nd
and 3rd stage projects is shown. With a lower production limit, the timing at which Groningen drops of
plateau is delayed and subsequently the installation of compression is also delayed. This figure shows the
technical optimal timing for the base case model. For different sub-surface realisations this timing can be
slightly different.

Curtailment First 2nd-phase First 3rd-phase
scenario
renovation
renovation

Reference
40
30

Figure 3.11

2016
2016
2018

2021
2021
2023

The impact of curtailment scenarios on gas production in Bcm/year and on the timing of the
compression projects. The reference scenario is based on expected market demand.

Figure 3.12 shows for different curtailment scenarios the forecasted pressure of two production clusters, ’t
Zandt (ZND) in the North and Zuiderveen (ZVN) in the South. From the figure the following observations
can be made:




Depletion in 2012-2014 was some 6 bar/year.



30 Bcm/year curtailment reduces depletion in the South to almost 2 bar/yr and in the North to 4
bar/yr in the period 2014-2020. These numbers are very consistent with the earlier discussed general
depletion trends per region based on P over Z plot (4*6+2*3=30 Bcm/yr).

Under the current implemented production philosophy, curtailment has the largest impact on clusters in
the South of the field. The pressure decline for clusters in the North of the field is less affected. This
effect is most noticeable before 2025. After 2030, the pressure decline rates for the various
curtailment scenarios converge.

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ZVN

Market

Figure 3.12

40 bcm/year

ZND

30 bcm/year

Market

40 bcm/year

30 bcm/year

Effect of curtailment on pressure depletion with the current production philosophy.

Under a production philosophy based on equalisation of pressure over the field, curtailment has its largest
impact on the pressure in the South of the field. Under reduced offtake scenarios the pressure is higher in
the South of the field.
3.4.2

Depletion behaviour under an alternative production philosophy

An alternative objective of curtailment could be the reduction of the annual depletion in areas where
earthquakes occur more frequently. In Figure 3.13, the field is divided in three areas with different
seismicity. The alternative production philosophy could be implemented by preferentially producing the
clusters in the area with no or only infrequent earthquakes (Area 1). In periods with increased demand,
also clusters in the area with occasional earthquakes are taken into production (Area 2). The clusters in the
area with frequent earthquakes (Area 3) are only taken into production during periods with high demand.
As a negative consequence the pressure difference between northern and southern part of the field will
increase.
The Eemskanaal-cluster is an exception, as it produces gas of a different quality. This gas is blended with
normal Groningen gas and hence the amount of gas produced from the Eemskanaal cluster is determined
by how much the other clusters produce. In practice, the Eemskanaal cluster produces at a below average
rate (<40%).

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3
2

1

Figure 3.13

Three areas of the Groningen field with different levels of seismicity. The alternative production
philosophy aims to reduce the pressure drop (and therefore compaction) in the earthquake prone areas
of the field (area 3) and preferentially produce the areas which are less susceptible to earthquakes (area
1).

The forecasted pressure response to the alternative production philosophy for different curtailment scenarios
is shown in Figure 3.14. The two production clusters, ’t Zandt (ZND) in the North and Zuiderveen (ZVN) in
the South respond very differently. From the figure the following observations can be made:




Depletion in 2012-2014 was some 6 bar/year.



A 30 Bcm/year curtailment reduces depletion in the North to some 2 bar/yr and in the South to some
4 bar/yr in the period 2014-2020. The reduction in the depletion in the North of the field is only
temporary as beyond 2020 the depletion stabilises to some 4 bar/yr.



Under the implementation of the alternative production philosophy, curtailment has the largest impact
on clusters in the North of the field, while the pressure decline for clusters in the South of the field is less
affected. This effect is most noticeable before 2025. After 2030, the various pressure decline rates
converge.

The pressure difference between North and South will increase to some 30-35 bar (see Figure 3.15).

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ZVN

Market

Figure 3.14

40 bcm/year

ZND

30 bcm/year

Market

40 bcm/year

30 bcm/year

Effect of curtailment on pressure depletion under the alternative production philosophy.

current production philosophy

alternative production philosophy
30 bcm/year

Figure 3.15

Pressure difference in the field for the 30 Bcm/year curtailment scenario assuming the current (left) and
the alternative production philosophy (right).

The alternative production philosophy will also impact the timing of the future development projects, more
specifically the installation of additional compressors. In Figure 3.11 the results of several production
curtailment scenarios and the impact on the Groningen 2 nd and 3rd stage compression projects are shown.
A consequence of the alternative production philosophy is that compression is required earlier in the South.
However, in the North the requirement for compression is delayed. In practise, this means that instead of
an implementation schedule based on 3 clusters per year an implementation schedule of 2 clusters per year
is required.
The effect of the different production philosophies under a 30 Bcm/year curtailment scenario is most clearly
demonstrated by Figure 3.18. With the current production philosophy the pressure difference between the
North and the South of the field is reduced. The initial excess capacity in this scenario makes the pressure
difference drop to close to zero. After 2023 the pressure difference increases again due to the reduced
production flexibility, when the capacity has declined to the curtailment level.
The alternative production philosophy shows the opposite effect. Following from this philosophy, the
pressure in the North is maintained high while the pressure difference with the South increases to some 30
bar. The pressure difference remains at this level for about ten years and then declines as the field off take
declines.

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North/south pressure difference
60

Pressure difference [bar]

50
40
30
20
10
0
-10
-20
1963

1973

1983

1993

2003

2013

2023

2033

2043

2053

2063

Year

Figure 3.16

3.4.3

Historical

Market demand, std. allocation

30BCM p.a., std. production policy

30BCM p.a., alt. production policy

Development of the pressure difference between the north and the South of the Groningen field for
different production philosophy’s with a 30 Bcm/year curtailment. The market scenario (green),
30Bcm/year curtailment using the current depletion philosophy (yellow) and the 30Bcm/year
curtailment using the alternative depletion philosophy (red) are shown.

Effect of subsurface uncertainty on depletion behaviour

In general with stronger aquifer support of the gas reservoir, the pressure decline in the aquifer will be less,
but felt in a larger area of the area of the aquifer. This will impact the compaction of the aquifer rock. The
uncertainty in the aquifer behaviour therefore impacts compaction and subsidence predictions.
In the history matching process the reservoir model is conditioned to reproduce the measured pressure data
throughout the production history. A very extensive data set of more than 1700 pressure measurements in
the gas column is available. The history matching process results in a well-conditioned model, which can
serve to predict future reservoir pressure with confidence. The ability to reproduce the measured pressure
data is expressed in the RMS (Root Mean Square) of the difference between measured and simulated
pressure data. For the best model realisation this is some 1.7 bar, while in practise models with a RMS< 3
bar are considered to be acceptable matches. Thanks to the large pressure data set, the pressure match
and pressure prediction in the gas column of the field is reliable over the larger part of the field.
The reservoir pressure in the gas column is well understood, but the pressure in the water leg is poorly
constrained. This is mainly due to the lack of data from the water leg. Such data (e.g. from repeat
formation tests) can only be acquired immediately after drilling of the reservoir section of a well. As over
the last decennia only a few wells have been drilled, most of the available RFT data is from the seventies
and eighties. In Figure 3.17 an example of available RFT data is shown.

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RFT Measurements UHZ-1 (02/09/78)
2800
P
P

22 bar

2850

ROCY 7.2

P

ROCY 7.1

Fair: seal failure

Measured Depth (m TVNAP)

ROCY 6

2900
ROCY 4

2950

3000

2 bar

ROCY 2

35 bar

3050

3100

3150
275

285

295

305

315

325

335

Measured Pressure (bara)
RFT Pressures
Bottom Ameland

Figure 3.17

Top Slochteren
Reported Average GWC

Bottom Slochteren
Gas Gradient: 0.177 bar/10m

Top Ameland
Water Gradient: 1.150 bar/10m

Example of RFT data from well Uithuizen-1 showing a pressure difference between the gas- and water
leg.

The uncertainty in the pressure behaviour of the aquifer is particularly large in the North-West periphery
where only a few observation wells are located (see Figure 3.18).
A good pressure match with historical pressure data in this area can be obtained with different levels of
pressure support from the aquifer. This can have a significant influence on the future pressure prediction in
this area (Figure 3.19). In other parts of the field, the well density is larger and hence the uncertainty is
smaller.

Figure 3.18

Pressure prediction based on market demand assuming different NW aquifer strengths for a well in the
NW (ZRP) and a well in the South (ZVN4) of the field.

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Weak Aquifer

Moderate Aquifer

FWL

Figure 3.19

3.5

East-West Cross section through well Zeerijp showing different pressure realisations. The colours
indicate reservoir pressure in bar(red 350 bar and light blue 95 bar). With a moderate aquifer the
reservoir pressure in the water leg is higher by some 20 bar.

Pressure Maintenance

Pressure maintenance as a general concept is based on reinjection of a volume equal to the gas volume
production from the reservoir. By balancing the injected and produced volume, the pressure in the gas
reservoir can be stabilised. Since compaction is thought to be a key factor in the creation of seismicity, this
would also reduce seismic activity in the Groningen area. Pressure maintenance is expected to arrest
reservoir compaction, and hence to reduce the seismic hazard. However, to what extent is yet to be
established through further geomechanical modelling. Especially the impact on seismicity of injection into
or close to a fault is a concern. The current insights have mainly been obtained with the geomechanical
sub-models and are described in section 9.
A description of a base case re-development of the field for pressure maintenance is included in this report
as appendix 12. This shows the dimensions and impact of such a re-development of the Groningen field.
Here we include only a short summary.
Based on its abundant presence, relatively inert behaviour and tolerance for presence in the sales gas,
nitrogen was chosen as the optimal injection medium. Pressure maintenance using injection of nitrogen
has been studied, supported by a base case development concept and production forecast. Analyses have
been based on the Groningen reservoir model, and on the experience in modelling of gas–gas
displacement and mixing in the reservoir in the underground storage projects of Norg and Grijpskerk, but
the remaining uncertainty in the production forecasts and recovery is large.
Breakthrough of nitrogen in producer wells as a consequence of pressure maintenance is expected to
significantly reduce Groningen gas reserves compared to continued depletion of the reservoir. Large
electricity consumption further reduces the gas volume available for sales.
The scope of the project is far beyond the scale of current global experience (5 – 7 times larger than the
largest similar project ever installed). Although the contours and main dimensions of a Pressure
Maintenance Project have been sketched, technical feasibility of the project has not yet been established.
Installation of nitrogen injection takes at least 5 years to first injection and some 9 years to ramp up to full
capacity. Cost of the project is estimated at 6 to 10 bln EUR.
The project carries a material environmental footprint, with significant land use and electricity consumption.
Societal support for a large-scale nitrogen injection project is expected to be a key risk.
This study is in an early stage assessment of the pressure maintenance recovery concept. Optimization is
still possible for a number of aspects. Injection of water is not considered a feasible and reliable pressure
maintenance option to reduce seismic activity.

3.6

Scenarios

A number of scenarios have been investigated, combining different geological realizations, development
plans and production options (Table 3.1).

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Two geological models were used, i.e. the best history matched model based on reservoir pressure (G1)
and a model with additional changes in the porosity and aquifer strength in the northwest of the field to
improve the match of the modelled subsidence with the measured subsidence data (G2).
The development scenarios are based in incrementally adding well drilling projects. The base development
includes the compression projects and the hook-up of a single well in the Kolham area of the field (D1). In
the next two cases the development of the north western area of the field (D2) and the south western area
of the field (D3) are successively added.
The curtailment scenarios investigated are the market driven production scenario (C1) and curtailment of
gas production to 40 Bcm/year (C2) and 30 Bcm/year (C3). A production case where the production is
ceased (C4) is also included. This case is sometime referred to as the “emergency stop scenario”.
Within the curtailment levels the different production philosophies are implemented. The current philosophy
is based on maintaining the reservoir pressure differences in the main area of the field as small as possible
(A1). The alternative philosophy to keep reservoir pressure preferentially higher in the north than in the
south by managing the gas offtake from these areas (A2) is also included. The development of the field for
implementation of pressure maintenance based on injection of nitrogen (A3) is described in a separate
report (Ref. 1).
For the modelling of compaction and subsidence three compaction models were used: the time-decay
model (S1), the isotach (1st cycle) model (S2) and the bi-linear model (S3). This is further described in
section three of this report.

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Table 3.1

Different geological, development and production cases used in the preparation of the different
scenarios for assessment of subsidence and seismic hazard.

Geological Realisations
G1

Model realization with Moderate NW aquifer

G2

Model realization with Weak NW aquifer

Development Scenarios
D1

KHM1 hook-up, 2nd & 3rd stage compression

D2

D1 + North Western Periphery development

D3

D2 + South Western Periphery development

Curtailment Scenarios
C1

Market Demand

C2

40 bcm/year

C3

30 bcm/year

C4

No production

Alternative Philosophy
A1

Current Production Philosophy

A2

Alternative Production Philosophy

A3

Pressure Maintenance

Subsidence Model

Table 3.2

S1

Time decay

S2

Isotach (1st cycle)

S3

Bi-linear

Scenarios for which pressure development in the reservoir, compaction at reservoir level and seismic
hazard has been assessed.
Petroleum Engineering
Nr

Geo R

Dev Sc

Subsidence
Curt

Alt Phil

Compaction Models

1

G1

D1

C3

A1

S1/S2/S3

2

G1

D1

C2

A1

S1/S2

3

G1

D1

C1

A1

S1/S2/S3

4

G1

D2

C1

A1

S1/S2

5

G1

D3

C1

A1

S1/S2

6

G1

D1

C3

A2

S1/S2

7

G1

D1

C2

A2

S1/S2

8

G1

D1

C1

A2

S1/S2

9

G1

None

C2

A3

S1/S2

10

G1

No

C4

None

S1/S2

Based on the different cases in terms of geology, development, production and subsidence model, a
number of scenarios were prepared (Table 3.2). For each of these scenarios the pressure development in
the reservoir, the compaction at reservoir level (Section 4) and seismic hazard (Section 8) are assessed.

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3.7




Conclusion
Two different production philosophies lead to different pressure distributions over the field. The effects
on seismicity will be addressed in Section 4.
Reservoir pressure predictions in the gas column are well constrained by the historical pressure data
and the reservoir model. Closer to the water leg the uncertainty in the reservoir pressure is generally
larger. This is expressed in larger local uncertainty in the reservoir compaction and seismicity.

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4 Historical compaction / subsidence and future trends
4.1

Summary

For the Groningen subsidence calculations three compaction models were investigated: the bi-linear, time
decay and isotach model. The bi-linear model was the preferred compaction model in NAM until 2011
and has been used in subsidence forecasts to calculate the subsidence for the Groningen winningsplan of
2007. When updating the Ameland winningsplan in 2010 it became apparent that the bi-linear model
could not describe the ongoing subsidence observed above this field (Winningsplan Ameland, 2011) with
decreasing depletion rates. This ongoing subsidence was the reason to develop the time decay
model,which showed a good fit between measured and modelled subsidence, with only a small number of
reasonably well constrained parameters. The time-decay model for Ameland gives a good fit with the
subsidence measurements whilst applying a Cm-porosity trend line based on the Cm values from
laboratory data (Cm-lab) taken from the Slochteren Formation.
The same approach was followed to match the subsidence above the Groningen field. In this case,
however, the same Cm-porosity trend line could not be used directly. To match the subsidence above
Groningen, a multiplication factor of about 0.5 to this trend line had to be applied.
A third model was adopted from TNO (Pruiksma et al. 2013) called the linear isotach model. This model
provides a good match with the historical subsidence data but contains more degrees of freedom. It also
allows for a history matched scenario with implementation of the laboratory base Cm-porosity trend line.
This scenario leads to an even more extensive continuation of subsidence and is considered at the moment
as a worst case scenario.
Reservoir pressures from the Groningen MoReS model have been used as input for the compaction model.
The distribution of reservoir porosity and thickness were taken from the static Petrel model and upscaled
with the condition that the upscaled compaction is equal to the sum of the compaction of the individual
layers.
A RMS method was used to check the “goodness-of-fit” between modelled and measured historic
subsidence.

4.2

Introduction

The Groningen field was discovered in 1959 and gas production started in 1963. Globally, the field
belongs to the top-ten largest gas fields. Subsidence was recognised as a risk before production and from
the start, subsidence data was collected regularly through levelling surveys. Since 1993 also satellite based
Interferometric Synthetic Aperture Radar (InSAR) measurements are available.
Over the years the subsidence models have been revised and refined as more data became available.
Compaction models have to meet two basic criteria: they have to closely match the subsidence
measurements and they should be based on plausible physical mechanisms. Section 4.3 will describe the
availability of data, both laboratory compaction data and geodetic data. Section 4.4 outlines the
geomechanics of the Groningen field describing the compaction models and the importance of overburden
behaviour. The next sections (4.5, 4.6 and 4.7) describe model calibrations and the subsidence forecasts.

4.3

Geodetic and geomechanical data

In this section two sources of data relevant for model calibration are described. First an overview will be
presented of the geodetic data, followed by a description of the geomechanical data (Cm measurements).
Other data such as geological data and pressure data have been described in Sections 2 and 3.
4.3.1

Survey protocol

According to regulations in the Dutch mining law (of 2002), a survey plan is required for all onshore
gas/oil production activities.

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The State Supervision of Mines (Staatstoezicht op de Mijnen, SodM) has to be informed every year on the
status of the survey plan, whether or not changes have been made and which geodetic surveys have been
planned for the coming year.
The frequency at which the surveys will be carried out is contained in the Survey plans.
For Groningen the frequency for a full levelling survey is once every 5 years.

Prognosis
Report

Inspection
Report

Inspection

Meetplan
Benchmark
Data

Prognosis
Winningsplan /
Opslagplan

Analysis
Report

Measurement

Basis

Network
Maintenance

Analysis

Meetregister

The initial survey for Groningen was carried out in 1964 but covered only the southern part of the field.
The first full levelling survey for the entire area was in 1972.
Levelling surveys are executed according to the procedure as defined by SodM and Rijkswaterstaat DataICT Dienst (RWS-DID) per 18 August 2005.
Levelling networks are designed such, that the benchmarks at the edges of the network are just outside the
subsiding area.
Results of the survey are reported in the official and publicly available Survey Register. The Survey register
consists of a free network adjustment (1st phase) as a QC on the observations, a state of differences with
relative heights (relative to the chosen reference benchmark) and height differences of the benchmarks
between epochs and a map with the survey network and benchmarks, labelled with the height differences
between the last and previous epoch. The presented height differences are not corrected for possible
autonomous movement. They present the total displacement at surface. Identifying the cause (deformation
due to gas extraction, autonomous movement or otherwise) can only be carried out by expert analysis and
is not part of the survey register.
4.3.2

Surveying techniques

Current surveying techniques are:
-

Spirit levelling
PS-InSAR (Satellite Radar Interferometry)
GPS (as part of GNSS: Global Navigation Satellite System)

Spirit Levelling
This technique has been used for Groningen since 1964.

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Surveys are executed according to regulations defined by RWS-DID as stated in ‘Productspecificaties
Beheer NAP, Secundaire waterpassingen t.b.v. de bijhouding van het NAP, versie 1.1 van januari 2008’.
The equipment used includes certified, self registering, optical levelling instruments and barcode level staffs.
Measurements are registered fully automatic in a registration and validation system defined by RWS-DID.
PS-InSAR
Since 2010, deformation based on PS-InSAR technique is reported, in conjunction with a number of
levelling trajectories for validation.
Deformation is estimated from phase differences between the acquisitions and persistent scatterers
(Hanssen, 2001). The spatial resolution depends on the presence of natural reflectors, such as buildings.
To obtain a precision comparable to levelling, error sources (like atmospheric disturbance, orbital
inaccuracies) need to be estimated and removed. To support this, a time series of satellite images is
required (>20-25 images) and ample resolution of scatterers. The estimated deformation velocity from
InSAR observations is 0.5-2 mm/year (see Ketelaar, 2009).
The big advantages of the InSAR technique are its high temporal resolution (> 10x per year) and the dense
spatial resolution. No survey crew is required in the field, hence no disturbance of the area and no security
risks. Moreover, the accuracy of PS-InSAR is comparable to levelling.
4.3.3

Survey design

Levelling network
The Groningen levelling network is part of the bigger Northern Netherlands network. Figure 4.1 displays
the levelling network, as surveyed in 2008.

Figure 4.1

Levelling network Groningen field

PS-InSAR
The persistent scatterers (PS) have a constant reflection in time and space and correspond merely with
buildings in the terrain. Below Figure 4.2 shows in green dots the persistent scatterers as detected in the
descending Radarsat-2 track.

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Figure 4.2

4.3.4

Spatial coverage with Persistent Scatterers in North-East Netherlands on the basis of images from
descending Radarsat-2 track

Levelling and Insar data used for the calibration

The calibration is done on the “differentiestaten”, a list of differential heights between the benchmarks per
epoch of the levelling campaigns. The measured height differences of the levelling surveys are referenced
with the geodetic program Move3 using the reference point A2080 nearby Gasselte (Drenthe). Data from
the latest levelling survey in 2013 came in too late (November 2013) to be included in the calibration
procedure described in this report.
The benchmarks within the area bounded by a purple square indicated in Figure 4.8 are used. RMS values
were calculated for each benchmark as described in section 4.6.1. 8 out of 1000 benchmarks were
excluded from the dataset as they were showing a very high RMS (higher than 7) value and showing a
temporal subsidence pattern that is in disagreement with the subsidence behaviour observed in
neighbouring points.
Two sets of levelling data have been used to calibrate the model. The first set only contains data that were
recorded in the full levelling campaigns. These datasets are: H_04_1964, H_09_1972, H_09_1975,
H_09_1985, H_08_1987, H_05_1990, H_05_1991, H_06_1993, _06_1997, H_06_1998, H_06_2003,
H_08_2008.
Next to the levelling surveys also Insar surveys were used. Instead of using the Insar data as a separate
dataset the Insar data was integrated with the levelling data (Figure 4.3). These combined datasets used in
the calibration are: D_16_12_1993, D_16_12_1994, D_16_12_1995, D_16_12_1996, D_16_12_1997,
D_16_12_1998, D_16_12_1999, D_16_12_2000, D_16_12_2001, D_16_12_2002, D_16_12_2003,
D_16_12_2004, D_16_12_2005, D_16_12_2006, D_16_12_2007, D_16_12_2008, D_16_12_2009,
D_16_12_2010, D_16_12_2011.

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Figure 4.3

Integration of the Insar data (black squares) with the levelling data (red points)

Two datasets became available too late to be used for the calibration. These surveys are: H_07_1978,
H_07_1981. They are shown for completeness in Figure 4.30 together with the 2013 survey.
4.3.5

Data from uniaxial compaction experiments

Subsidence measurements are the primary data source used in calibrating the geomechanical models. In
addition, compaction experiments on plugs taken from reservoir core samples can also provide insights into
the constitutive behaviour of the reservoir rock.
However, conclusions should be drawn with care. The sparse sampling density of core material cannot fully
constrain the spatial variability of the reservoir compressibility. Similarly, the total compaction of the
reservoir formation is a function of the reservoir thickness and the changes in pore fluid pressure, and these
are not always known very accurately. In recent years much consideration has been given to the unloading
of the core confining stress during exhumation, which can lead to the possible development of microcracks, thus making the samples more compressible. The expectation is that such ‘softening’ would be
especially marked during the first cycle of a multi-cycle compression test. But there are many other sources
of uncertainty. Therefore, the models should first match the subsidence data, while the reservoir
compressibility should fall within the range of measured plug data.
ROSL samples
The Groningen Cm-values [*10-5bar-1] compare well with all other available data on ROSL plugs. Figure
4.4 plots these values as a function of porosity.. A best-fit cubic polynomial trend line with porosity fraction
[-], using a least-squared regression based on all data (L2 norm), is also plotted in the graph. Based on the
good agreement between Groningen data and the overall ROSL data, it was decided to use this regression
fit as a starting point for the calibration of the geomechanical model to the subsidence measurements:

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Figure 4.4

Comparison of the Groningen data to all available ROSL Cm values as a function of atmospheric
porosity

Also, raw stress-strain curves are available for some of the Groningen plug tests. These measurements were
used to determine values for the elastic parameters where a linear relation was observed during the test. In
the case of linear elasticity, the M modulus is the inverse of the Cm. In the dynamic domain the M modulus
(also called P-wave modulus) is determined by:

with ρ the bulk density and Vp the P-wave velocity. As the log porosity is also closely related to the sonic
velocity it is physically sound to investigate the relationship between the M-modulus determined from lab
Cm measurements and the measured atmospheric porosity and use a possible trend line from this domain
to compare it with other trend lines. The Young’s modulus was calculated for the selected tests where the
raw stress-strain curves were still available and where a linear relation was observed during the test. A
numeric inversion module in the finite element simulator Geomec was used to invert for the Young’s
modulus and Poisson’s ratio.
Figure 4.5 shows the Young’s modulus and M modulus as a function of porosity with a linear trend line
based on an L2 norm.
Figure 4.6 shows in blue the curve for the Cm-porosity relation based on the linear M-por trend line in
Figure 4.5. This curve is close to the Cm-porosity calibrated curve based on the subsidence data. These
different trend lines actually indicate the scatter of the data and the difficulty in concluding whether or not a
model fits to the Cm dataset.

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Figure 4.5

Elasticity and M modulus as function of porosity

Figure 4.6

Comparison of Cm derived from M linear trend line compared to data and other trend lines

4.4

The Groningen Geomechanical model

The Groningen geomechanical model calculates the compaction at reservoir level and transfers the derived
strains to surface subsidence by using a semi-analytic approach (Geertsma 1973 and Geertsma and van
Opstal, 1973), incorporated in Shell’s ‘SubCal’ software.
The compaction model has the same dimensions as the MoReS reservoir simulation model. The extent of
this compaction model is shown in Figure 4.7. The areas in blue indicate the location of the aquifers which
were explicitly modelled in Mores.

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Figure 4.7

Outline of the geomechanical model; green indicates the gas-bearing part, and blue the water-bearing
part included in the Mores model.

The geomechanical model uses the top reservoir map, the reservoir thickness, the reservoir pressure and the
porosity as an input for the calculations. The model consists of one single reservoir layer instead of the 40
Mores layers and therefore an upscaling method is applied that is documented in section 4.4.4.
In the calibration to the measured subsidence, the effect of subsidence caused by neighbouring fields is
accounted for by modelling these fields with a separate subsidence model. Only the subsidence
measurements above the Groningen field (Figure 4.8) are used to compare with the calculated results.

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Figure 4.8

Area definition used for benchmarks used in the calibration (within the purple square). The red
benchmark indicates benchmark 007E033 which is referred to in section 4.4.1.

The compaction at reservoir level is calculated with three different compaction models, the bi-linear, timedecay and isotach model, which are outlined in the next paragraphs. Calibration of the models to the
subsidence data is described in sections 4.5 and 4.6
4.4.1

Linear and Bi-linear Compaction Models

The laboratory compaction measurements of Rotliegend rock consistently show a linear pore pressure strain relationship, for conditions appropriate to gas fields in the Netherlands. This in turn, should lead to a
linear relationship between pressure depletion and subsidence, assuming that the surrounding formations
behave in a predominantly linear elastic manner. However, when plotting the subsidence of a benchmark
near the centre of the field versus the reservoir pressure (Figure 4.9) two straights lines can be
distinguished, suggesting a bi-linear shape compaction curve. This bi-linearity has also been observed in
other fields, like Ameland and Roswinkel. These observations are the basis of the bi-linear compaction
model.

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Subsidence vs Reservoirpressure at benchmark 007E0033
400

350

300

Reservoir pressure (bar)
250

200

150

100

0

5

Subsidence (cm)

10

15

20

25

30

35

Figure 4.9

Comparison of subsidence measurements (blue dots) with reservoir pressure for Benchmark 007E0033 .
The lines show a bi-linear compaction when starting linear compaction for start and one from the end.

In Figure 4.9 the pressure at which the two compaction lines meet is called the Transition pressure (Ptrans).
The Cm value used for pressures between the initial and transition pressure is called Cmpre and the Cm value
used for reservoir pressures smaller than the transition pressure is Cmpost.
In general the Cm [*10-5 bar-1] value is a function of porosity [-]. In the bi-linear model the following
porosity Cm relations are used:

The Cmpre and Cmpost values found by calibration of the subsidence model to the measured subsidence are
not related directly to the core measurements, but their values are in the range of the laboratory
measurements of the 2nd and 1st cycle compaction experiments. The bi-linear compaction model was used
for subsidence forecasts in the Statusreport 2010 (NAM, 2010) and in the winningsplannen of the North
Netherlands fields.
A supporting hypothesis was developed in which the bilinear behaviour was explained by geological
burial and subsequent partial exhumation. Formation damage of the core during drilling and extraction
was invoked as an explanation of why the laboratory samples failed to display the bilinear behaviour and
as a justification for using second cycle compressibility measurements for the initial reservoir stiffness (it
should be realised that the ‘initially stiff’ reservoir compressibility was only very poorly constrained by
subsidence data). However, further research studies, designed to test and characterise this bilinear law
have been unconvincing.
Later in 2011 it appeared that calibration of Ameland subsidence measurements to the bi-linear model
parameters returned in very high post transition pressure Cm values, higher than the laboratory ones. Close
examinations of the measurements made apparent that this misfit was caused by ongoing subsidence whilst

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the depletion had almost ceased. To solve this, a more physics based explanation was sought and found in
the Time decay model.
4.4.2

Time Decay Compaction Model

The observation of a delayed, slowly accelerating subsidence, at the onset of pressure depletion, in
combination with continuing subsidence after depletion has ceased, is consistent with a time lag (time
decay) process; where the subsidence response to reservoir compaction is asymptotic, with a characteristic
time decay constant. Processes of this type are fundamental and commonplace throughout the natural
world; they are the signature of non-equilibrium dynamical systems. The archetype of processes in this class
is the familiar diffusion or heat equation.
The fundamental nature of time decay processes makes it highly likely that this is the underlying physical
mechanism. Indeed, one is already integrated into the subsidence modeling workflow; the main task of the
reservoir simulation and history match is to solve a pressure diffusion problem, a fact that makes
deficiencies in the reservoir modeling stage a very tenable explanation.
Time decay type models have been proposed as explanations for subsidence delay in the past.
Houtenbos [pers. comm., 2006] proposed a simple empirical time decay relationship between ‘subsidence
volume’ and the mass of gas produced. A number of errors in the physical reasoning led to a rejection of
this proposal by NAM and SodM. It was observed at the time though, that transfer functions of this type did
appear to provide a satisfactory temporal match to subsidence data and that they are characteristic of a
diffusive, and therefore physically reasonable, process. A distantly related time dependent process was
contained within the Rate Type Compaction Model [RTCM] [de Waal, 1986], which also sought to explain
observed subsidence delay above a number of reservoirs. This was adopted for a number of years by
NAM, although subsequently rejected on the basis of divergence from observed data and subsequent
critical internal review.
The resultant of a combination of independent time decay processes is itself a time decay process. This fact
and the very universality of non-equilibrium dynamics makes it difficult to precisely identify the part or parts
of the system that govern the decay time scale. The most obvious and likely is that pressure diffusion effects
are not properly captured in the reservoir and aquifer modelling step. Salt creep is also a time dependent
non-equilibrium process that has long been considered potentially significant in subsidence modelling and
could also provide a possible explanation, given the sparseness of subsidence data coverage. There is also
evidence of time dependent compaction processes in consolidated rock, these seem too small to be
significant, but they are poorly understood and might scale with size in some non-linear manner. A rather
more esoteric mechanism based on the more highly structured pressure depletion patterns that would result
from more realistic conductivity distributions and a reinterpretation of poroelastic volume strain is
promising. There are doubtless others not yet considered.
However, it is perfectly possible to accurately characterise the governing parameters of a time decay
process and make reliable predictions, without having to know the exact causative origins. Based on this
reasoning, the difficulty of identifying the exact contributory mechanism or mechanisms, and the paucity of
in-situ data, NAM proposed to use a simple first order diffusive time decay function, and apply inversion to
find the single time constant this required.
Volumetric Time Decay
While it cannot be claimed that the precise cause of the volumetric time decay process has been identified,
it seems highly likely that it is associated with volume strain in the reservoir rather than elsewhere in the
subsurface. The constrained volume strain, eii, at a point, x, in the reservoir is then the usual instantaneous
product of pressure change, Δp, and constrained uniaxial compressibility, cm, but now convolved with a
time decay function.

Here, t, is time, *t, is the convolution operator with respect to time and, τ, is a time decay constant. The best
fitting time decay constants for the Groningen field were found by inversion using a semi-analytic

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geomechanical model and have values of some 3 to 8 years. More sophisticated modelling would not have
any significant impact on this result.
It should be realised that it is quite possible that the observed time decay is not a material property of the
reservoir rock, but could be due to particularities of the reservoir geometry, pore fluids or some other
factor. Therefore it cannot be assumed that the time decay constant appropriate for one reservoir can be
applied to another based simply on rock type.
As has already been emphasised, the observation of a time decay effect and the ability to characterise and
build predictions using it, are not dependent on precisely identifying the causative mechanism. However,
for the sake of the record, a brief description will be made of one of the proposed mechanisms.
4.4.3

Linear Isotach Compaction model

In the Netherlands, this model is the most accepted model for settlement calculations in soft soil. The model
is also known as the a,b,c isotachen model (den Haan, 2003). TNO (Pruiksma et al., 2013) studied the
application of this model on cemented rock using the laboratory experiments from de Waal as a starting
point (De Waal, 1986). Adjustments of this a, b, c istoachen model led to the definition of the linear isotach
model which was also implemented by NAM and used to calibrate the compaction model and estimate
future subsidence.
Figure 4.10 shows the isotach model as a spring-dashpot system (Pruiksma et al., 2013)

Figure 4.10

Representation of the isotach model

The total strain in the isotach model is calculated by the sum of the direct and elastic strain (
secular strain ( ) with
the effective stress:

) and the

Where:
and
Pruiksma et al. 2013 proposed to linearize this model as it was demonstrated that this provided a better fit
to the laboratory experiments:
This implies that:

and

The term
solved for:
,

,

can be derived from the depletion, leaving four other parameters that need to be
and

is coupled to the viscosity :

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4.4.4

Upscaling of input data

The reservoir modelling has been performed using Shell’s reservoir simulator MoReS. It uses a 3dimensional gridded reservoir structure, which includes porosity and permeability information and attempts
to solve for the fluid flow and pressure changes as constrained by historic production and pressure data
taken from wells. The reservoir model therefore contains self consistent porosity and pressure change
information that should be used in the geomechanical modeling. However, the reservoir model tends to use
a finer grid size than is needed for the geomechanical model so ‘upscaling’ is required.
For the Groningen field, a MoReS reservoir model was built consisting of 40 vertically stacked layers and
with grid dimensions of 250 m by 250 m, or less. The reservoir formation in the Geomechanical model is
made up of one single layer with a lateral element size of around 500 m by 500 m. It is therefore
necessary to up-scale the MoRes parameters for the geomechanical model. This is achieved by postprocessing of the MoRes output file using routines coded in the general purpose mathematics package,
Scilab. Figure 4.11 shows an example of a MoReS output file. The files contain the grid pressures for a
single time step and a separate file is generated for each time step.

Figure 4.11

Example of a Mores output file, la indicates the layer number.

Upscaling of the reservoir pressure has to take into account the the variability in the porosity and pressure
depletion between the vertically stacked reservoir layers, and weigh them accordingly. This is achieved by
summation of the reservoir layer compaction as a function of the layer pressure depletion and
compressibility, the latter being a function of porosity. This upscaled compaction has to be equal to the
compaction derived for the upscaled layer. The porosity of this upscaled layer is:

Where
and
are the porosity and thickness for each reservoir model layer, respectively. Summation is
over the appropriate number of grid layers.
Upscaling the pressure change is less straightforward. The objective is that the up-scaled compaction using
a Δpavg is the same as the compaction from the non up-scaled model. The effect of compaction in the
individual layers should therefore be included in the upscaling procedure.
The change in thickness of each reservoir model layer is given by:

Where
is the compaction coefficient as a function of the layer porosity and Δpi is the pressure
change in layer, i. The aggregate change in reservoir thickness across the vertically stacked layers is given
by:

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where H indicates the stacked layer thickness. The upscaled pressure across the stacked thickness,
then given by:

, is

This upscaled pressure was found to be insensitive to the detailed functional form of the compressibilityporosity relationship.

4.5

Comparing the finite element model with Geertsma and van Opstal, the possible influence
of rock salt creep

In this section, the subsidence calculation results from two different models are compared: a finite element
model (FEM) and the analytical Geertsma and van Opstal model. The objective of this comparison is to
illustrate the impact of viscous salt deformation on the subsidence. If the effect on the subsidence is small,
the analytical model can be used for further predictions.
4.5.1

Zechstein salt creep

Rock salt behaves like a highly viscous non-Newtonian fluid. Many investigators have proposed constitutive
models where strain rate is a function of differential stress (usually a power law) and absolute temperature
(usually exponential). Power law equations of the type presented below are widely accepted and used to
calculate deformation for a wide range of geotechnical problems such as cavern closure [e.g. Weertman,
1955; Heard, 1972; Munson and Dawson, 1979].

Here the shear strain rate, , is a function of: absolute temperature, T, shear stress, σ, a shear stress
normalisation term, σ0, and the gas constant, R. Values for the scaling parameters, Ai, the activation
energies, Qi, and the power law terms, ni, are then determined from fits to experimental data.
These constitutive equations can be simplified by realising that different creep mechanisms are dominant
for different stress and temperature domains [Munson, 1979]. Of particular importance for this study is the
observation that a pressure solution creep mechanism becomes the dominant flow law at low values of
differential stress and moderate temperatures [Spiers et al., 1986; Spiers and Carter, 1998]. This obeys a
simple unitary (i.e. linear) power law dependence on shear stress, i.e. it is a Newtonian fluid.

where A =B/Td3 (with d is the effective grain size length scale, and B a material constant). Hence the creep
constant (A) is highly dependent on the grain size of the salt.
There is precedent in the application of linear salt creep to geomechanical models in this region. Breunese
et al. [2003] observed that such linear pressure solution creep could explain the temporal and spatial
pattern of the subsidence above two deep salt caverns in the northern part of the Netherlands.
Representative grain sizes, found in cores from the Zechstein, range from 3 mm to 20 mm length scale. The
stress normalization term, σ0, is set to 1 MPa while Q/R corresponds to 3007 K. The temperature gradient
is 0.03 K m-1 with a reference temperature of 373.15 K (100⁰C) at 3000 m TVDSS.
However, the relative insensitivity of the scaling parameter, A, compared to the exponential term, permits
the use of a constant mean temperature (378.15 K, 105⁰C) in its determination, see Table 4.1. This range
was then applied to the Zechstein layer elements in a numerical geomechanical model to compare with the
analytical solution that doesn’t account for salt behaviour (section 4.5).

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Grain size salt [mm]
3
20
Table 4.1

A [s-1]
3.94E-08
1.39E-10

Grain size and associated scaling parameter, A, range used in models.

The FEM contains, besides the reservoir layer itself, a description of the layers of the over- and
underburden. Rather than assuming a rigid basement in the analytical approach we explicitly model as
accurate as possible the stiffness contrasts between reservoir and burden. The finite element model was
tested for salt creep applying two sets of salt flow parameters. Another run was made using linear-elastic
behaviour of the salt. Finally, the possible effect of element type (linear hexahedron versus linear
tetrahedron) was tested for the “medium salt” case. The bottom depth of the finite element model was set to
7000m. Mechanical parameters for the different layers used in the finite element model are tabulated in
Table 4.2.
Unit

Young’s
Modulus

Poisson's
ratio

Density

Remarks / non-linear parameters

-3

[kg m ]

[GPa]
North Sea

2

0.3

2150

Chalk

10

0.25

2350

16

0.25

2300

30

0.35

2200

Lower
Cretaceous
Triassic

/

Zechstein

Q/R

3007 K

A

1,69E-09 s-1 (medium salt)

A

1.39E-10s-1 (slow salt)

Or linear model
Ten Boer
Claystone

40

0.2

2300

Rotliegendes

1.-40
(base
case)

0.2

2300

ROSLU
ROCLA

E derived from Cm*

ROSLL

In undrained area: E = 44 GPa

Carboniferous
*

In drained area depending on the porosity
(fraction):

40

0.2

2300

Cm is uniaxial compaction coefficient [10-5 bar-1]

Table 4.2

Used parameters in finite element model for over- and underburden layers.

The Cm for the analytical model is based on the same relation with porosity as listed in Table 4.2. Both
models used a time-decay corrected pressure input with an arbitrary time-decay of five years.
The lateral boundary of the finite element model is set at about one reservoir size (40 km) of the reservoir
(Figure 4.12) to minimize boundary effects on the displacement.

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Figure 4.12

Extend of finite element model indicated by the red box.

The difference between these models is shown in Figure 4.13 to Figure 4.15. Figure 4.13 shows the
subsidence contours for the analytical and Geomec model for 2008 compared with the subsidence results
from the FEM using a medium fast salt. The figure shows that the contours are quite close to each other
meaning that the impact of the salt on the subsidence bowl is very limited.
620000

615000

610000

605000

600000

595000

590000

585000

580000

575000

570000

565000

560000
220000 225000 230000 235000 240000 245000 250000 255000 260000 265000 270000 275000

Figure 4.13

Comparison of the subsidence contours from the analytical model (dashed) and Geomec model(line) for
the year 2008.

Figure 4.14 and Figure 4.15 show the subsidence of the analytical model (line) and the finite element
models using three salt flow models (dots) near the deepest point of the subsidence bowl and in the NorthWest Aquifer respectively. The finite element models are calculated for a limited number of years (dots).

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1950
0

1960

1970

Year
1980

1990

2000

2010

5
Analytical

Medsalt Hex

Subsidence (cm)

MedsaltTet

10

Slowsalt
No Salt

15

20

25

Figure 4.14

1950
0

Subsidence versus time near the deepest point (x=240000 y=591000) for Geomec models and
analytical model
1960

1970

Year
1980

1990

2000

2010

5
Analytical

Medsalt Hex

Subsidence (cm)

MedsaltTet

10

Slowsalt
No Salt

15

20

25

Figure 4.15

Subsidence versus time in the NW aquifer (x=237000 y=603000) for Geomec models and analytical
model (Geertsma and van Opstal)

Finally, Figure 4.16 shows a comparison between the modelled subsidence for the year 2008 of the
analytical model (Geertsma and van Opstal) and the finite element model (Geomec) using different salt
parameters.

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Geomec (cm)

35
30

Medsalt Hex

25

MedsaltTet
Slowsalt

20
No Salt

15
10
5
0
0

Figure 4.16

5

10

15
20
Geertsma and van Opstal (cm)

25

30

35

Comparison subsidence calculation between Analytical(Geertsma and van Opstal) vs Finite element
models(Geomec) for 2008

Conclusion
As the shape of the subsidence bowl between the analytical and finite element model is very similar and the
difference in calculated subsidence, also between the different salt realisations, is small, we conclude that
the subsidence calculation with the analytical model will give sufficiently accurate results.

4.6

Calibration of the model

For the purposes of subsidence prediction and preparation of the Winningsplan a representative, or ‘base
case’, model must be selected. Throughout this document there are references to improved model fits and
matches to subsidence data. This section describes the method used to make these judgements and the
results for the different compaction models.
4.6.1

RMS of all benchmarks using epoch combinations

A relatively simple comparative measure of model fit was created by determining the residuals between the
surveyed and modelled subsidence at all benchmarks for a number of different time spans, termed epochs,
and calculating the total variance weighted root-mean-squared (RMS) value. A trivial sequential set of
epochs can be envisaged, shown schematically in Figure 4.17(a), where the last survey of a previous epoch
becomes the first survey of the next epoch. However, the comparatively small subsidence signal between
subsequent levelling campaigns means that this approach would suffer from low signal-to-noise ratio. A
comprehensive scheme including all possible epoch combinations could also be considered. Figure 4.17(b)
schematically represents all the epoch combinations that include the first levelling campaign and similar
epoch sets can be generated for each campaign, while excluding duplicates. This again though includes a
large number of epochs with low signal-to-noise ratio while being considerably more computationally
expensive. Instead a robust metric was created using a standardised subset of epochs that were of sufficient
duration to provide good signal-to-noise ratio while keeping computation to a moderate level. These are
shown schematically in Figure 4.17(c).

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a)
Figure 4.17

b)

c)

Schematic showing epoch combinations used in the calibration process: a) sequential, b) all possible
combinations (example shows combinations with survey 1 only) c) efficient method used in current
analysis.

Figure 4.18 shows an example plot where measured and modelled subsidence are plotted against each
other for a variety of epochs and the calculated RMS value given.

Figure 4.18

4.6.2

Example of ‘RMS’ graph showing the modeled subsidence against the measured subsidence, in cm.

Basecase model

For the three compaction models a calibration was made with the levelling data. Starting from the best fit
(lowest RMS), a Monte Carlo simulation was done to illustrate the sensitivity of the different compaction
parameters. This simulation showed that for the bilinear and time decay model the Cm value is well
constrained. The range of Cm values which give acceptable RMS results for the isotach model is large.
As basis a first cycle Cm [10-5 bar-1]-porosity [-] relation is used,
[10-5 bar-1]
For the purpose of calibrating the model to the subsidence, a
multiplication factor:

is introduced that is basically a

) [10-5 bar-1]
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In the next paragraphs the results are discussed in more detail for the various compaction models.
4.6.3

Results for the Bi-linear model

The calibration of the bi-linear model resulted in the following parameters for the compaction model
showing the lowest RMS value according the method explained in section 4.6.1.

A comparison of the measured versus modelled subsidence for the full levelling surveys is shown in Figure
4.19, using these values for
,
and .

Figure 4.19

Measured vs. modeled subsidence for the full levelling and insar surveys (1996-2011) for the bi-linear
model

To investigate the uniqueness of this solution a Monte Carlo simulation to the bi-linear compaction
parameters was done. The parameters investigated were
,
and
whilst was kept constant
-5
-1
at 0.11x10 bar .
The results are shown in Figure 4.20. The colours in this figure represent a certain RMS range with in green
the range for RMS values < 1.4. In the left graph the best
and
values are around 1.5 and 3,
respectively. In the right graph only the best
and
(RMS < 2) values are plotted. The colours in
this plot now represent the Transition pressure. In the area where RMS for
and
are lowest the
colours for the transition pressure are dominated by red and blue colours indicating an optimal
value between the 225 and 250 bar.

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Figure 4.20

Monte Carlo analysis for the bi-linear compaction model. The left graph shows the
versus
the color indicates the Rms value of the combination. The right graph plots the
versus
now the color indicates the transition pressure.

,
but

To show the distribution of possible average Cm values for 20% porosity which resulted in a RMS < 2 in the
Monte Carlo simulation a histogram was made (Figure 4.21). The Cm values are calculated for the full
production period (till the end pressure of 10 bar)

Figure 4.21

Histogram of Cm values calculated at 20% porosity for bi-linear models with an RMS < 2

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4.6.4

Time decay

The calibration of the time decay model resulted in the following parameters for the compaction model
showing the lowest RMS value according to the method explained in section 4.6.1.
: 0.45
decay time : 7.3 year
A comparison of the measured versus modelled subsidence for the full levelling surveys is shown in Figure
4.22.

Figure 4.22

Measured vs modelled subsidence for the full levelling and a insar surveys (1996-2011) for the time
decay model

To investigate the uniqueness of these parameters, a Monte Carlo simulation was carried out. The results of
this analysis are shown in Figure 4.23. In this figure, the Cm factors are plotted against the resulting time
decay values (in years) where the colours indicate the RMS value. An in Figure 4.24 the resulting Cm
values at a porosity of 20% from the Monte Carlo combinations of a RMS < 2 are shown. These figures
show a narrow bandwidth for the Cm but a wider bandwidth for the . The red dot indicates the
parameters used for the winningsplan model. The wide bandwidth for the could be further constrained by
making a comparison between the measured data in benchmark 7E33 with several time decay models with
a variable (Figure 4.25). For this case, the
was adjusted to match the modelled subsidence in
2013 to the measured subsidence. A of around 7 years shows the best fit with the measured data.

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Figure 4.23

Monte Carlo analysis for the Time Decay compaction model. The dot shows the Cm and Time decay
parameters used in the winningsplan model.

Figure 4.24

Histogram for the Cm values at 20% porosity from the Monte Carlo combinations with a RMS < 2

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Figure 4.25

Effect of time decay on the subsidence at point 7E33 (the location of this point is indicated as a red dot
in Figure 4.8 )

Conclusion
With a limited set of parameters that could be reasonably well constrained by the result, the time decay
model is able to produce a reasonable fit with the observed data. Therefore we treat this model as the base
case model.
4.6.5

Isotach model

The Isotach model, as described in section 4.4.3, has four parameters. To investigate the uniqueness of
possible parameter combinations a Monte Carlo simulation was done. The results are shown in Figure
4.26. In these figure the RMS value is plotted on the y-axis, the split between a low (blue) and a higher
RMS value (yellow) was arbitrarily set at a RMS of 2. The figure shows that, out of the four available
parameters, only the
is reasonably constrained by the results (top left figure). The other
parameters can be regarded as unconstrained. This means that a very broad range of parameter
combinations is possible that all will give a good fit with the measured subsidence data. This implies that
the future subsidence forecast will have a wide range as well. Note that for the
and the
the
logarithm of these values is used, meaning that the distribution of these parameters is even larger.

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Figure 4.26

Monte Carlo analysis for the Isotach compaction model.

The resulting Cm factor is the addition of both the Cm1 and Cm2 factors. The large range of possible Cm
values, at 20% porosity, is shown in Figure 4.27. For comparison the laboratory derived Cm value is
1.5 *10-5 bar-1 and the calibrated value found with the time decay model is 0.7 *10-5 bar-1.

Figure 4.27

Histogram for Cm values at 20% for RMS < 2

Two cases were selected out of the wide distribution of possible realizations:
1. A realization where
+
=1, meaning that the total Cm is based on the Cmporosity trend line of the laboratory data. We regard this case to be a “worst case”
2. A realization where
+
equals the Cm factor of the time decay model
The realization of the isotach compaction model where the Cm value is fixed to the lab Cm value
(
+
=1) has resulted in the following model parameters:
= 0.32
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= 0.68
=1.09 108 [bar.yr]
= 0.7 10-6 [year-1]

A comparison of the measured versus modelled subsidence for the full levelling surveys is shown in Figure
4.28. The calibration with the laboratory Cm returns in a good fit (Figure 4.28) with the measured data.
Next paragraph shows that also a good fit can be obtained when a Cm value that equals the time decay
Cm is being used.

Figure 4.28

Measured vs. modeled subsidence for the full levelling and insar surveys(1996-2011) for the isotach
model

2. A realization where
+
equals the Cm factor of the time decay model
(
+
= 0.449) has resulted in the following model parameters.
factor = 0.23
factor = 0.22
= 0.34 108 [bar.yr]
= 0.9 10-6 [year-1]

The calibration plot is of this realisation shown in Figure 4.29 and is slightly better than the results of the
isotach realization using the laboratory Cm value.

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Figure 4.29

4.6.6

Calibration plot for the isotach model calibrated to the timedecay Cm value

Comparison

As shown in previous paragraphs the three compaction models show a good agreement with the measured
subsidence after a calibration. The time decay model is our base case model because of the limited number
of input parameters. Using the same Cm value in the various models results in more or less similar values
for the forecasted subsidence as shown in Figure 4.30. The isotach model with a fit of the Cm to the
laboratory measurements predicts significantly more subsidence.

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1960
0

1980

2000

Jaar
2020

2040

2060

2080

10

Bodemdaling (cm)

20

30

40
Timedecay
Bilinear
50

Isotach Laboratory Cm
007E0033

Isotach with Time Decay Cm value

60

70

Figure 4.30

4.6.7

Modeled vs. measured subsidence for the different compaction models in a point near the centre of the
subsidence bowl (red dot in Figure 4.8). The Isotach model has two realisations, one with the laboratory
Cm value and the other with the Cm value used in the Time Decay model.

Conclusion

The time decay compaction model is chosen as the base case model because of the limited amount of
reasonably well constrained parameters involved, and because of the good fit with the levelling data.
Furthermore, the model also gives a good fit to the subsidence observed above the Ameland and Anjum
Fields (NAM 2011a and NAM 2011b). Especially the better match with the continuing subsidence above
the Ameland field under decreasing rate of depletion makes this model superior to the bi-linear compaction
model which cannot reproduce this delayed compaction behavior.
The isotach model is the most flexible model giving a slightly lower RMS value than the other models. The
drawback of this model at present is that the four parameters in the model are poorly constrained resulting
in a wide range of possible subsidence forecasts.

4.7

Subsidence forecasts

As discussed in section 4.6, three different compaction models were used for history matching and for
forecasting the subsidence with ongoing production. The time decay model provides the base case model
while two alternative realisations are made using the isotach model: one that includes the Cm-porosity
relation representing the average laboratory observations and another applying the Cm-porosity relation
adopted from the calibrated time decay model. Section 4.6 and Figure 4.30 have shown that the three
compaction models result in a similar value for the subsidence at a point near the centre of the subsidence
bowl.
The modelled base case subsidence and the measured subsidence for the year 2008 are compared in
Figure 4.31. Overall, the match with the observed measurements is reasonable to good. Figure 4.32 shows
the forecast of the subsidence at the end of the field life using the same base case model. A maximum
subsidence of some 46 cm is forecasted with this model.

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Figure 4.31

Measured subsidence (1972 - 2008) at benchmarks versu modelled subsidence since 1964 (contours)
for 2008. The red dot indicates the benchmark 07E0033 which is the position for the comparison in
Figure 4.8.

Figure 4.32

Subsidence prognosis based on the time decay model at the end of field life. This figure shows the
subsidence due to gas production for the Groningen field in combination with the subsidence predicted
for neighboring fields (cm).

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The development of the future subsidence through time for the different compaction models is shown in
Figure 4.33 to Figure 4.36. As shown in section 4.6 the bi-linear and time decay compaction models lead
to comparable Cm values, which in turn leads to a comparable subsidence development (Figure 4.33 and
Figure 4.34). Two variations are run for the isotach model. Figure 4.35 shows the result of the model with
average laboratory data values for the Cm and Figure 4.36 shows the results with Cm relation adopted
from the time decay model.

2013-2018
Figure 4.33

2013-2080

Development of the to be expected subsidence according to the time decay compaction model

2013-2018
Figure 4.34

2013-2023

2013-2023

2013-2080

Development of the to be expected subsidence according to the bi-linear compaction model

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2013-2018
Figure 4.35

2013-2080

Development through time of the prognosed subsidence based on the isotach compaction model, and
using the laboratory measured Cm value.

2013-2018
Figure 4.36

2013-2023

2013-2023

2013-2080

Development through time of the prognosed subsidence based on the isotach compaction model, and
using the Cm values determined by calibration with the time decay model.

The future prognosis of the various compaction models leads to similar results when using the same Cmporosity relationship. Only the Isotach model using the higher laboratory Cm value results in a much higher
subsidence. This case his considered to represent a “worst case” prognosis.

4.8

References

Breunese, J.N, van Eijs R.M.H.E., de Meer, Kroon I.C. (2003). Observation and prediction of the relation
between salt creep and land subsidence in solution mining – The Barradeel case. Proc. SMRI Conf.,
Chester, UK.
de Waal, J.A. (1986). On the rate type compaction behaviour of sandstone reservoir rock. PhD thesis,
Technische Hogeschool, Delft.

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E.J. den Haan, Het a,b,c –Isotachenmodel: hoeksteen van een nieuwe aanpak van zettingsberekeningen,
Geotechniek, oktober 2003
Geertsma, J. (1973) , Land Subsidence Above Compacting Oil and Gas Reservoirs, J. Petr. Tech., pp.734744.
Geertsma, J. and van Opstal, G. (1973). A Numerical Technique for Predicting Subsidence Above
Compacting Reservoirs, Based on the Nucleus of Strain Concept. Verh. Kon. Ned. Geol. Mijnbouwk. Gen.,
28, pp. 63-78.
Hanssen, R. F. (2001), Radar Interferometry: Data Interpretation and Error Analysis, Kluwer Academic
Publishers, Dordrecht.
Heard, H.C. (1972). Steady-state flow in polycrystalline halite at pressure of 2 kilobars. AGU Geophysical
Monograph Series, 16: pp. 191-210.
J.P. Pruiksma, J.N. Breunese, K. van Thienen-Visser (2013) A general framework for rate dependent
compaction models for reservoir rock. TNO 2013 R11405
Ketelaar, V. (2009), Satellite radar interferometry – subsidence monitoring techniques, Springer,
Dordrecht, ISBN 978-1-402-09427-9
Munson, D.E. (1979). Preliminary Deformation-Mechanism Map for Salt (with Application to WIPP).
SAND79-O076, Sandia National Laboratories.
Munson, D.E., and P.R. Dawson. (1979). Constitutive Model for the Low Temperature Creep of Salt (with
Application to WIPP). SAND79-1853, Sandia National Laboratories.
NAM (2010) “Bodemdaling door Aardgaswinning –NAM-velden in Groningen, Friesland en het Noorden
van Drenthe– Status Rapport 2010 en Prognose tot het jaar 2070” (EP201006302236)
NAM (2011a). Subsurface Technical Report Subsidence Modelling of Ameland Fields. UIE Report No:
EP201105208617
NAM (2011b). Subsurface Technical Report Subsidence Modelling of Anjum and Waddenzee Fields. UIE
Report No: EP201105208677
Spiers, C.J. & Carter, N.L.(1998) Microphysics of rocksalt flow in nature. In: M. Aubertin & H.R.Hardy
(eds.) The Mechanical Behaviour of Salt: Proceedings of the Fourth Conference Serieson Rock and Soil
Mechanics, 22, TTP Trans Tech Publications, Clausthal-Zellerfeld, pp.115-128.
Spiers, C. J., J. L. Urai, G. S. Lister, J. N. Boland, and H. J.Zwart. (1986). The influence of fluid-rock
interaction on the rheology of salt rock: Nuclear Science and Technology,EUR 10399 EN, 131 p.
Weertman, J. (1955). Theory of steady-state creep based on dislocation climb. J. Appl. Phys. 26 (10),
1213–1217.

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5 Illustrative examples of induced seismicity from other
locations
5.1

Introduction

The largest earthquakes are associated with tectonic movements but earthquakes of small and intermediate
magnitude can, in some cases, be induced by human activities. Induced seismicity has been linked to
mining, the filling of (surface) reservoirs and engineering operations for oil and gas extraction, geothermal
energy production and waste fluid disposal. Current seismicity observed in the North of the Netherlands is
generally accepted as being induced by gas production from the Northern gas fields – Groningen and
others.
In section 6.2 the geomechanics underlying production induced seismicity is discussed. The magnitude of
the resulting earthquakes will be determined by the size of the slip plane, the amount of slip occurring and
the rock properties. The amount of seismic energy radiated will also be dependent on the change in stress
which occurs or, equivalently, the speed of propagation of the earthquake rupture across the fault plane.
Modern theories of friction (Scholz 1998) explain and distinguish between the unstable so-called stick-slip
motion on faults characterizing earthquakes and stable sliding or creep motion by which stresses can be
released without seismicity.
In some cases it is possible that pore pressure changes and compaction or inflation could trigger the
release of pre-existing stress in the subsurface – this scenario is sometimes referred to as triggered
seismicity in recognition of the fact that the stresses being released are not solely due to the triggering
process itself. Triggered seismicity might be expected in tectonically active areas of the world.
Reviews of induced seismicity have been presented in the open literature by (among others) Guha (2000),
Suckale (2010), Klose (2013) and the Committee on Induced Seismicity Potential in Energy Technologies
(2013). These reviews present lists of known cases coming from the oil and gas industry and other energy
industry sectors such as geothermal and hydroelectric power production. It is clear from these lists that
although Groningen is one of the better known cases of a gas field exhibiting induced seismicity it is by no
means the only such field. In the majority of cases however, oil and gas production appears to proceed
without any observed induced seismicity.
The following paragraphs give a brief summary of some of the more relevant cases of induced seismicity
documented in the open literature. A similar summary can be found in Dost and Kraaijpoel (2013). The
cases chosen below are discussed as being illustrative of certain relevant aspects of inducd seismicity. It
should however be emphasized that none of the known cases is analogous to the Groningen field in every
respect (geological setting, lithology, size, etc.) and as such there is a limit to the applicable lessons which
can be learned from the study of analogues. The purpose of this summary of other cases is to place the
Groningen field example in a broader seismological context and so show that Groningen’s induced
seismicity is not without precedent.
5.1.1

Gazli

The most extreme case known of seismicity associated with gas production is from the Gazli field in
Uzbekistan: between 1962 and 1984 a number of earthquakes with magnitudes of up to about 7 occurred
on a large thrust fault complex, to the NE of the field (Simpson and Leith 1985, Grasso 1992 and Amorèse
et al 1995). Details given in the literature are limited but it is generally quoted as an example of induced
seismicity. It could however be argued that the earthquakes may have been tectonic in origin, possibly
triggered by production of the gas field. While the true nature of the Gazli events will probably remain
open for debate it should be seen as an illustration of the possible scale of induced or triggered seismic
events.

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5.1.2

Lorca

In 2011 a shallow (about 3km) magnitude 5.1 earthquake occurred close to the Spanish town of Lorca,
apparently triggered by prolonged ground water extraction (Avouac 2012). Much damage was done to
buildings and there were 9 deaths. Although not an example associated with extraction of hydrocarbons,
this case does show that moderate magnitude induced or triggered earthquakes can cause significant
damage at surface if sufficiently shallow.
5.1.3

Rotenburg

In 2004 a magnitude 4.4 event occurred at a depth between 5 and 7 km, probably induced by gas
production from the nearby Söhlingen or Rotenburg gas fields in Germany where the depth of the
Rotliegend reservoir is 4.5 – 5 km (Dahm et al. 2007). Also being Rotliegend gas fields, Rotenburg and
Söhlingen are particularly relevant examples, but their sizes are not comparable to the Groningen field.
5.1.4

Lacq

At the Lacq gas field in Southern France more than 2000 local events with magnitudes of up to 4.2 were
detected using two permanent local seismic networks between 1974 and 1997 (Bardainne et al 2008).
Larger events with magnitude up to 5.7 sometimes attributed to the field (Klose 2013) are more likely
natural seismicity associated with the tectonics of the Pyrenees, some 20 – 30km farther to the South (Dost
and Kraaijpoel 2013, Segall et al 1994 and Bardainne et al 2008 but see also Grasso 1992; Klose 2013
does not distinguish between induced and triggered events). Although Lacq is another gas field exhibiting
significant levels of induced seismicity it is not a good analogue for Groningen given the differences in
tectonic setting (close to a mountain range) and lithology (the reservoir is a carbonate).
5.1.5

Yibal

This field in Oman comprises a producing gas reservoir undergoing significant pressure depletion and
compaction (surface subsidence rates up to 50 mm per year are observed), and a deeper producing oil
reservoir under water flood. Integrated interpretation of geodetic data (InSAR and GPS) and passive
seismic data (from both deep down-hole and shallow buried arrays) has lead to the recognition that major
fault systems crossing the field are being reactivated by depletion of the gas reservoir leading to differential
compaction across faults (Bourne et al 2006). Large numbers of induced microseismic events have been
detected with relatively high magnitudes (two-thirds of all located microseismic events are larger than
magnitude -1.5) and some of the largest events have been felt at surface. The data shows that fault slip is
not limited to the depleting gas reservoir interval but extends significantly above and below below this.
5.1.6

Ekofisk

Ekofisk is a Norwegian North Sea oil field which has experienced significant sea bed subsidence (more
than 8m at the crest of the structure – see Ottemöller et al 2005) due to production from the initially overpressured chalk reservoir. A magnitude 4.4 earthquake occurred on 7th May 2001 and was strongly felt on
platforms in the area but did not cause damage to facilities or wells. Detailed analysis revealed (Ottemöller
et al 2005) that the event appears to have been induced by accidental fluid injection into the overburden,
rather than directly by the reservoir compaction itself.
5.1.7

Fashing

Observed events in the Fashing field in Texas of up to magnitude 4.3 are considered to be induced by oil
and gas production from the Edwards Limestone formation (Keahey 1968) reservoir at a depth of about
3400m (Klose 2013, Davis et al 1995 and Frohlich 2012). On 20th October 2011 a larger, magnitude 4.8
earthquake occurred near Fashing. This event’s possible origins are complicated by the fact that there are
fluid disposal wells within a few km of the reported event location.
5.1.8

Other hydrocarbon fields in the Netherlands

Of the approximately125 producing hydrocarbon fields in the Netherlands only 23 are known to be
seismically active (van Thienen-Visser et al., 2012). Besides Groningen, only in two other fields, i.e.
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Bergermeer and Roswinkel, events with magnitude of 3.0 and above (up to M=3.5) have ocurred. Van Eijs
et al (2006) argue that fault density, pressure depletion and stiffness contrast between the reservoir and
seal rocks are the key parameters which correlate with the occurrence of observed seismicity in the
hydrocarbon fields in the Netherlands.

5.2

Concluding remarks

Referring to tables 5.1 and 5.2 in section 5.4 which summarize the known cases of induced seismicity in
the Shell Group, it can be seen that felt seismicity induced by oil or gas extraction is rare, at least in the
Shell Group’s portfolio. Detailed analysis of the many apparently aseismic cases would be required to
understand this in terms of the rock and fault properties and production histories. The potential for induced
seismicity, particularly in and around the gas fields in the North of the Netherlands, is however properly
recognized.

5.3

References

Amorèse, D., Grasso, J.-R., Plotnikova, L.M., Nurtaev, B.S. and Bossu, R. Rupture Kinematics of the Three
Gazli Major Earthquakes from Vertical and Horizontal Displacements Data. Bulletin of the Seismological
Society of America (1995), Vol. 85, No. 2, pp. 552-559.
Avouac, Jean-Philippe. Earthquakes: Human-induced shaking. Nature Geoscience (2012) 5: 763–764.
Bardainne, T., Dubos-Sallée, N., Sénéchal, G., Gaillot, P. and Perroud, H. Analysis of the induced
seismicity of the Lacq gas field (Southwestern France) and model of deformation. Geophys. J. Int. (2008)
172, 1151–1162.
Bourne, S.J., Maron, K., Oates, S.J. and Mueller, G. Monitoring Reservoir Deformation on Land - Evidence
for Fault Re-Activation from Microseismic, InSAR, and GPS Data. 68th EAGE Conference & Exhibition
(2006).
Committee on Induced Seismicity Potential in Energy Technologies. Induced Seismicity Potential in Energy
Technologies. The National Academies Press (2013).
Davis, S. D., Nyffenegger, P. and Frohlich, C. The 9 April 1993 earthquake in south-central Texas: Was it
induced by fluid withdrawal? Bulletin of the Seismological Society of America (1995), 85 1888-1895.
Dahm, T., Krüger, F., Stammler, K., Klinge, K., Kind, R., Wylegalla, K. and Grasso, J.-R. The 2004 Mw 4.4
Rotenburg, Northern Germany, Earthquake and Its Possible Relationship with Gas Recovery. Bulletin of the
Seismological Society of America (2007), Vol. 97, No. 3, pp. 691–704.
Dost, B. and Kraaijpoel, D. The August 16, 2012 earthquake near Huizinge (Groningen). KNMI report,
KNMI De Bilt, January 2013.
Frohlich. C. Induced or triggered earthquakes in Texas: assessment of current knowledge and suggestions
for future research. University of Texas at Austin Technical Report G12AP20001 (2012).
Grasso, J.R. Mechanics of Seismic Instabilities Induced by the Recovery of Hydrocarbons. Pure and Applied
Geophysics, Vol. 139, No. 3/4 (1992)
Guha, S. K. Induced earthquakes. Kluwer (2000).
Keahey, R.A. Fashing Field, Atascosa-Karnes Counties, Texas. In Natural Gases of North America, Volume
One, AAPG Special Volumes (1968).
Klose, Christian D. Mechanical and statistical evidence of the causality of human-made mass shifts on the
Earth’s upper crust and the occurrence of earthquakes. Journal of Seismology (2013) 17: 109–135.
Ottemöller, L., Nielsen, H. H., Atakan, K., Braunmiller, J. and Havskov, J. The 7 May 2001 induced seismic
event in the Ekofisk oil field, North Sea. Journal of Geophysical Research (2005), Vol. 110, B10301.
Scholz, Christopher H. Earthquakes and friction laws. Nature (1998) 391(1st January 1998): 37-42.
Segall, P., Grasso, J.-P. and Mossop, A. Poroelastic stressing and induced seismicity near the Lacq gas
field, southwestern France. Journal of Geophysical Research (1994), Vol. 99, B8, pp. 15,423 – 15,438.
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Simpson, D.W. and Leith, W. The 1976 and 1984 Gazli, USSR, earthquakes – were they induced?
Bulletin of the Seismological Society of America (1985), Vol. 75, No. 5, pp. 1465-1468.
Suckale, Jenny. Moderate-to-large seismicity induced by hydrocarbon production. The Leading Edge
March (2010): 310 – 319.
Thienen-Visser, K. van, et al. (2012). Deterministische hazard analyse voor geïnduceerde seismiciteit in
Nederland. TNO-rapport 2012 R10198.
Van Eijs, R.M.H.E., Mulders, F.M.M., Nepveu, M., Kenter, C.J. and Scheffers, B.C. Correlation between
hydrocarbon reservoir properties and induced seismicity in the Netherlands. Engineering Geology (2006)
84: 99-111.

5.4

Tables

The following tables summarize the known cases of induced seismicity in the Shell Group. Table 5.1 lists the
cases in which felt seismicity is known (or, in the case of Belridge, considered likely) to be induced by oil or
gas extraction from fields in the Shell portfolio.
Table 5.1

Cases of felt induced seismicity in the Shell portfolio

Region/location/OU

Field

Largest recorded
event (M)

Type

Comments
(Mitigation etc.)

Netherlands (NAM)

Groningen

3.6 (Huizinge)

Induced by gas
production

Seismic
monitoring

Other gas fields
(20% of all Dutch
gas fields,
particularly
Annerveen,
Eleveld &
Roswinkel)

3.4 (Roswinkel)

Induced by gas
production

Roswinkel Field
abandoned

Gas production leads to compaction inducing earthquakes with normal fault mechanisms
Oman (PDO)

Yibal

2.24

Induced by oil &
gas production

Seismic
monitoring

Gas & oil production cause differential compaction reactivating Graben faults crossing field
Germany

Rotenburg
(partners via BEB)

4.4

Induced by gas
production

Seismic
monitoring

Normal faulting events at or below reservoir level, considered to be induced by gas production
California

Belridge (AERA)

No
seismic/earthquake
data available

Water flooding &
cyclic steaming
for production
from diatomite

InSAR &
microseismic
monitoring

Production from diatomite results in significant subsidence – (felt) induced seismicity is to be expected

Table 5.2 lists the geothermal cases in which Shell was involved and in which felt induced seismicity
occurred – these were JVs of which we are no longer a part but which have been well-documented in the
literature.

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Table 5.2

Historic Shell involvement in geothermal joint ventures with well documented felt induced seismicity

Region/location/OU

Field(s)

Largest recorded
event (M)

Type

Mitigation

El Salvador

Berlín

>3

Fluid
injection/fraccing

Seismic
monitoring and
traffic light system

France (Alsace)

Soultz-sous-Forêts

>3

Fluid
injection/fraccing

Seismic
monitoring

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6 Release of energy by Earthquakes
6.1

Analysis of the induced earthquake record

6.1.1

Frequency-magnitude distribution

Since the first earthquake recorded in the north of the Netherlands in December 1986, KNMI has compiled
a steadily growing catalogue of earthquakes. At the time of writing their report, Dost et al. (2012)
commented that the catalogue for the north of the Netherlands contained 640 events. The following
analysis is restricted to the 187 events with a magnitude of at least 1.5 recorded between 1st April 1995
and 30th October 2012. This start date corresponds to the earliest date for which the monitoring network
was capable of locating all earthquakes of at least magnitude 1.5. This end date reflects the data available
when technical studies commenced following the magnitude 3.6 Huizinge earthquake in August 2012.
Throughout this report we will refer to moment magnitude and local magnitude interchangeably because
KNMI asserts the local magnitude scale is essentially indistinguishable from the moment magnitude over the
observed magnitude range within a 95% confidence interval of ±0.2. However, this correspondence is
subject to an ongoing further investigation by KNMI (Dost & Kraaijpoel 2013) and may be revised in the
near future.
The number of observed earthquakes, N of at least magnitude, M, within a given region and period of time
follows the log-linear frequency-magnitude relationship known as the Gutenberg-Richter law (Gutenberg &
Richter 1954):
(6.1)
The b parameter, known as the b-value, describes the relative decline in abundance of larger magnitude
earthquakes relative to smaller ones and is therefore central to estimating the probability of larger
earthquakes occurring from an observed population of smaller earthquakes. Consider a monitoring system
that reliably observes all earthquakes within a given region of at least magnitude Mc (known as the
magnitude of completeness). If the b-value and Mmin remain unchanged, the probability, p1, that the next
independent observable earthquake exceeds magnitude M is:
(6.2)
For a future sequence of n independent observable earthquakes, it follows that the probability, pn, of at
least one exceeding magnitude M is:
(6.3)
This probability depends on the b-value. Various different methods for estimating the b-value from the 187
earthquakes within the Groningen Field all agree that b = 1.0 (Bourne & Oates 2012). Figure 6.1
compares this frequency-magnitude model with its 95% confidence interval to the observed frequencymagnitude distribution. Notice how the confidence interval is disproportionately larger for the rarer and
larger magnitude events that are the least well sampled within the catalogue. Consequently, although the
differences between this model and the observations are greatest for the larger magnitude events, none of
these differences is statistically significant given the small number of such events within the catalogue.
Monte Carlo analysis to investigate the range of different b-values consistent with these data indicate a
68% chance of 0.85 < b < 1.26 and a 95% chance of 0.68 < b < 1.5. Dividing these earthquakes into
subsets corresponding to different time intervals shows there was no statistically significant variations in bvalue with time.

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Figure 6.1

The frequency-magnitude distribution of earthquakes observed in the Groningen Field. Circles denote
the number of earthquakes within the magnitude interval M-0.05 to M+0.05. The thick black line
denotes the frequency-magnitude model of b=1.0. Grey shading denotes the 95% confidence region
associated with this model.

Earthquake populations are generally observed to follow the Gutenberg-Richter frequency-magnitude
distribution between lower and upper magnitude limits. The lower magnitude limit is typically the
magnitude of completeness, Mc. An upper bound, Mmax, on the frequency-magnitude distribution must also
occur since arbitrarily large events are physically impossible. Determination of such an upper bound from
an earthquake catalogue is however challenging because of the invariably poor statistics at the high
magnitude end of the distribution. Figure 6.2 shows an example of a truncated frequency-magnitude model
estimated from the observed frequency-magnitude distribution. This model is consistent with the data but so
are all other truncated models for Mmax > 3.9 including the initial model without any truncation.
Consequently, given the available data, frequency-magnitude analysis cannot establish a reliable upper
magnitude limit. This remains true even for all earthquakes within all gas fields in the north of the
Netherlands (Bourne & Oates 2012).
The maximum magnitude estimates of 3.2 to 3.9 from de Crook et al. (1995) and the subsequent KNMI
reports (de Crook et al., 1998; van Eck et al., 2006; Dost et al., 2012) obtained by fitting a truncated
frequency-magnitude model to the observed frequency-magnitude distribution all appear to share a similar
statistical error of over-fitting the model to the observed number of larger magnitude events by ignoring the
wider confidence interval of the models, as illustrated in Figure 6.2.

Figure 6.2

The cumulative frequency-magnitude distribution of earthquakes observed in the Groningen Field.
Circles denote the number of earthquakes of at least magnitude M. The thick black line denotes the
frequency-magnitude model of b=1.0. Grey shading denotes the 95% confidence region associated with
this model. The dashed line denotes an alternative frequency-magnitude model with b=1.0 and zero
probability of earthquakes above magnitude 3.9.

Table 6.1 and Figure 6.3 show that the probability of at least one earthquake exceeding magnitude 4 or 5
increases as the number of independent events of at least magnitude 1.5 increases in the future. These
estimates are subject to three conditions. First, the b-value remaining invariant with time so that the present

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range estimated from the historic data remains valid for future earthquakes. Second, minimum magnitude
of reliable earthquake monitoring does not exceed magnitude 1.5 for any reason over time. Third, the
probabilities only apply to the number of independent future earthquakes and these should not include any
dependent earthquakes such as aftershocks. Under these assumptions, the probability of at least one
earthquake exceeding local magnitude 4 during the next 30 locatable earthquakes within the Groningen
Field is 0.09 with a 68% confidence limit of 0.02 to 0.20.
There is no reliable empirical evidence to support a maximum magnitude as low as 3.9 from the
earthquake catalogue for the Groningen Field. Consequently, larger earthquakes are possible rather than
impossible as recognized by earlier studies (de Crook et al., 1998; Wassing et al., 2004; van Eck et al.,
2004; van Eck et al., 2006; Dost et al., 2012). This does not mean that there is no physical limit to the
maximum possible magnitude (see section 6.1.2), but rather the seismological data under consideration
here fail to provide any such constraint.

Table 6.1

Probability estimates for the occurrence of at least one earthquake of at least magnitude M within the
next n events of at least magnitude 1.5. The probability, p, is based on a b-value of 1.0. The 68% and
95% confidence intervals for these probabilities reflect the corresponding confidence intervals in the bvalue estimate of 0.85 to 1.26 and 0.68 to 1.50 respectively.

(a)

(c)

(b)

(d)

Figure 6.3

(a, b) Estimates for the probability of the occurrence of at least one earthquake within the Groningen
Field with a magnitude of at least 4 and 5 respectively. (c, d) The same as (a, b) except probabilities are
shown on a log-scale. These are based on Table 6.1.

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6.1.2

Physical limits on the maximum magnitude

In the analysis presented herein, it is assumed that all past and future earthquakes in the Groningen Field
are induced, by which it is understood that they are caused directly and entirely by anthropogenic activity
(i.e., gas extraction). The possibility of triggered earthquakes is not currently envisaged in the model.
Triggered earthquakes require that there are faults close to being critically stressed on which seismic slip is
imminent. For the seismicity model developed for the Groningen field, triggered seismicity is excluded on
the basis of the apparently very low natural seismicity, as revealed by both the instrumental and historical
records for the region. Houtgast’s (1992) catalogue of historical earthquakes in the Netherlands lists only
one event – on 28th January 1262 – in the north of the Netherlands. The historical record is however
unclear about the true nature of this event which may in fact have been a meteorological event rather than
an earthquake. The currently very low levels of natural seismicity in the northern Netherlands makes it
reasonable to assume that the field is free from incipient natural earthquakes caused by significant fault
ruptures.
There will be an upper bound to the maximum magnitude of earthquakes that can be induced by gas
production from the Groningen Field due to the finite elastic energy or strain associated with reservoir
compaction or the finite size of pre-existing faults. Estimates of these limits for the Groningen Field indicate
a maximum magnitude of M=6.5 if all strain accumulated over the life cycle of the field were released in a
single event at the very end of field life. Although an event of this size may be physically possible, the
probability of this to happen is essentially zero. Therefore, this magnitude represents an absolute physical
upper limit.
6.1.3

Finite strain limit

An earthquake is an abrupt slip on a fault surface as one side of the fault moves relative to the other. This
process releases elastic strain energy stored in the region surrounding the fault by transferring strain from
that region and localizing it along the fault. The largest possible induced earthquake corresponds to
releasing all the elastic strain associated with the ultimate reservoir compaction in a single event that
involves simultaneous slip on many faults.
One measure of the size of an earthquake is seismic moment. This is simply the product of the average slip
on the fault, the area that slipped, and the shear modulus which is a material constant describing the shear
stiffness of the surrounding medium. A mathematical consequence of this definition is that the average
strain due to slip on faults within a given volume is simply proportional to the total seismic moment per unit
volume (Kostrov 1974). In the particular case of reservoir compaction, this means the total seismic moment
is equal to the product of the bulk reservoir volume decrease and the shear modulus (McGarr 1976). Given
the current linear elastic model for ultimate reservoir compaction, the expected ultimate bulk reservoir
volume decrease is about 3.3 x 108 m3. For a representative shear modulus of 1010 Nm-2 (10 GPa) the
maximum total seismic moment that could be mobilized is 7.7 x 1018 Nm.
The relationship between the magnitude, M, and the seismic moment, Mo, takes the form

(6.4)
where typically c = 9.1 and d = 1.5 (Hanks & Kanamori 1979). So if the maximum seismic moment were
released during a single earthquake, this would be equivalent to a magnitude 6.5. Alternatively, if this total
seismic moment were released by a population of individual earthquakes with a b-value of 1, then the
expected largest earthquake would still be about magnitude 6.3 as the largest event is expected to release
about half the total seismic moment (Bourne & Oates, 2013).
6.1.4

Finite fault size limit

If future earthquakes are required to fit within the existing size of known faults then the largest of these
faults would limit the maximum magnitude. This approach was previously used to yield estimates for the
maximum magnitude of 3.9 for gas fields in the north of the Netherlands (Crook et al. 1998; MuntendamSubsurface aspects of Subsidence and Induced Earthquakes

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Bos et al. 2008). In reviewing this second study, Hager & Toksöz (2009) concluded the maximum
magnitude estimate depended entirely on an uncertain choice for the maximum fault slip area, although the
result of magnitude 3.9 was considered acceptable given its exact agreement with the maximum magnitude
obtained from frequency-magnitude analysis of the historic earthquakes (van Eck et al., 2006). However,
this result subsequently proved unreliable for the reasons described in section 6.1.1.
A magnitude 4 event may occur on an equi-dimensional fault surface of no more than 1.1 km by 1.1 km
for a 1 MPa stress drop and a fault slip of 90 mm (Bourne & Oates 2013). The fault height is poorly
constrained in the Groningen field. Nevertheless, there are numerous pre-existing faults substantially larger
than this mapped within the Groningen Field that could be associated with a fault slip area larger than
1.21 km (see Fig. 2.3). Consequently any argument based on the lack of sufficiently large pre-existing fault
surfaces cannot reliably constrain the maximum magnitude to be as low as M=4.
2

An alternative method is to infer the maximum slip area from estimates of the maximum slip and the
minimum stress drop (Bourne & Oates 2013). Although the maximum displacement is bound not to exceed
the maximum reservoir compaction, the minimum stress drop assumption of 1 MPa remains somewhat
uncertain. The resulting maximum magnitude estimate of 5.8 corresponds to a mean fault slip of 0.3 m on
a 60 km long fault spanning the long-axis of the field with a dip extent of 3000 m. The aspect ratio of this
slip surface is 20:1 which is large but not impossible given the range of aspect ratios observed for normal
dip-slip earthquakes (e.g. Leonard, 2010). There is no geomechanical requirement for the slip area to
remain inside the reservoir, although the Zechstein salt formation located directly above the reservoir may
prevent fault slip initiating above the reservoir. Uncertainty about the minimum stress drop means this
maximum magnitude estimate is less reliable than the previous estimate based on finite strain.
6.1.5

Finite mass limit

Using a world-wide catalogue, Klose (2012) obtained a statistical correlation between the observed
maximum magnitude of induced earthquakes and human-made mass shifts on the Earth's upper crust. For
normal faulting regimes such as within the northern Netherlands the regression between observed
magnitude and mass-shift obtained by Klose indicates an expected magnitude of 4.7 to 5 for a mass shift
of 2 to 2.5 Gt corresponding to an ultimate gas production of 2800 x 109 m3.
However, there is still substantial variation within the catalogue about this expected value so the upper
bound for this mass-shift will be somewhat larger. Although Klose did not estimate an upper bound, based
on the variation within the catalogue, a reasonable upper bound for the magnitude of an earthquake
induced by a 2.5 Gt mass-shift within a normal faulting regime is about M= 6. Further restricting the
catalogue to just those events due to gas extraction within a normal faulting regime the largest earthquake
in Klose's catalogue is the 1967 M=5.7 Arette (Lacq gas field) event in France (Segall et al. 1994).
However, it remains unclear if this event was induced by gas production or is part of natural seismicity
associated with the Pyrenees.

6.2

The earthquake strain model

Reservoir pressure depletion induces reservoir compaction, and together they induce poro-elastic stress
changes within and around the reservoir (Segall 1989). In some places the changes in the shear stress and
the effective normal stress acting on a pre-existing fault may be sufficient to equal its resistance to slip due
to its internal friction, cohesion, and initial stress state. Under these conditions the fault is critically stressed
and destabilized with no further resistance to slip (see section 9 for further details). These places are then
vulnerable to experiencing earthquakes. Within the reservoir normal faulting may be induced e.g. the Lacq
field (Segall et al. 1994) and the Ekofisk field (Zoback & Zinke 2002). Outside the reservoir reverse faulting
may be induced (Segall 1989). Within and outside the reservoir induced fault slip may localize along faults
subject to differential compaction, i.e. more compaction on one side of the fault relative to the other side of
the fault, e.g. the Eleveld field (Roest & Kuilman, 1994) and the Yibal field (Bourne et al. 2006).
By definition, critical stress is a necessary condition for induced seismicity but it is insufficient for two
reasons. First, knowledge of the internal friction, cohesion, and initial stress state of each fault is uncertain
and may vary considerably among individual faults. Second, even if a critical stress state is known it does
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not determine the amount and area of slip on the fault which governs the magnitude of any induced
earthquake.
The amount and area of slip on a critically-stressed fault depends on the average elastic strain within the
surrounding medium. Slip on a fault may be viewed as a mechanism for accommodating strain. During
fault slip some elastic strain within the surrounding medium is transferred to and localized along the fault
as a displacement discontinuity. Consequently, the average strain accommodated by a population of faults
that slipped is simply proportional to their total seismic moment per unit volume (Kostrov 1974). Knowledge
of the strain field therefore places an upper bound on the total seismic moment of earthquakes induced
under that strain field.
In the case of a compacting reservoir, the maximum total seismic moment scales with the bulk reservoir
volume change (McGarr 1976) under conditions where lateral gradients in the vertical strain are negligibly
small. This model represents the physical limit where slip on faults accommodates all induced strain.
Equivalently the gravitational potential energy released by reservoir compaction and subsidence also scales
with the bulk reservoir volume change and represents the physical limit where the mechanical work done
by slip on faults cannot exceed the released gravitational potential energy that was stored by elastic
deformations within and around the reservoir. The fault strain and the fault work descriptions are
fundamentally equivalent. For convenience, let us continue to describe the earthquake model in terms of
strain.
The observed total seismic moment may be significantly less than the maximum total seismic moment
because not all strain may be accommodated by fault slip and not all fault slip may be seismogenic. For
instance, elastic strains may remain in places where faults are not critically-stressed, ductile strains may
occur depending on the rheology and the strain rate of the medium, and faults may creep without radiating
seismic energy.
In the case of a compacting reservoir, subsidence measurements are able to reliably constrain a
geomechanical model for reservoir compaction with a lateral resolution comparable to the reservoir depth.
This means that sufficiently dense subsidence measurements are at best able to constrain the distribution of
average reservoir compaction on a length-scale of about 3 km. Knowledge of the strain field provided by
this geomechanical model places an upper bound on the total seismic moment of earthquakes induced by
reservoir compaction.
The fraction of strain accommodated by seismogenic fault slip cannot yet be predicted by geomechanical
models – but it can be measured in the field by comparing historic seismicity and subsidence observations.
6.2.1

Stress, compaction, faults and earthquakes

The current earthquake monitoring network is able to measure the epicenter of all earthquakes of at least
magnitude 1.5 with a standard error of 500 m (Kraaijpoel et al. 2008). Due to the geometry of this
network earthquake depths cannot in general be determined reliably and so event depth is routinely
assigned to a reservoir depth of 3 km (Kraaijpoel et al. 2008).
Earthquake epicenters during the period 1995 to 2012 (Figure 6.4) were preferentially located within the
central part of the field and were rare or absent elsewhere notably including the 100 km2 south-east corner
of the field. Let us now consider how this observed distribution of earthquake epicenters relates to the
distribution of critical stress, reservoir compaction, and pre-existing faults within and around the reservoir.
Critical stress
The stress state inside the reservoir is the sum of the initial stress state and the stress change induced by gas
production which essentially varies linearly with local reservoir pressure depletion. Pressure depletion is
essentially uniform through most of the reservoir (Figure 6.4, left panel). Despite equivalent levels of
pressure depletion in the central and south-east regions of the Groningen field, only the central region
experienced seismicity. Both regions experienced similar stress changes, but only one experienced
observed seismicity. One possibility is that the initial shear stress was less or the faults in the south-east are
stronger and therefore still able to resist slip. These are unlikely given the geological history, fault density

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and fault orientations are indistinguishable from the central region. Alternatively it seems generally more
plausible that some faults within the south-east were destabilized and seismogenic too. However, given the
relatively low levels of reservoir compaction in this area, the amount of slip on these faults is so small that
the associated earthquakes remain below the earthquake monitoring threshold of magnitude 1.5 and so
remained undetected. Critically stressed faults are then a necessary but insufficient condition for observable
seismicity within the Groningen Field.
Compaction and differential compaction
At any location within the reservoir, the average shear strains induced by reservoir compaction are
primarily dependent on the average local reservoir volume change. Lateral gradients in vertical strain may
also contribute, particularly the larger gradients that might exist due to differential compaction on either
side of pre-existing faults (e.g. Rest & Kuilman 1994).
Figure 6.4 compares the reservoir volume change per unit area (i.e. average reservoir compaction) and
lateral compaction gradients present with the current reservoir compaction model with the observed
earthquakes. Earthquake epicenters preferentially occurred within the places of greater reservoir
compaction (Figure 6.4, centre panel), but there is no evidence of similar clustering along the fault bound
lineaments associated with the greatest compaction gradients in the model or indeed any particular
mapped faults (Figure 6.4, right panel). Given the standard error of 500 m in epicenter locations
(Kraaijpoel et al., 2008) it remains uncertain if all true epicenters are located on mapped faults. Equally,
because the subsidence data are not sensitive to compaction gradients localized across faults, the true
distribution of compaction gradients remains uncertain.
The amount and sense of induced slip depends on the detailed geometry of the intersection between the
fault and the reservoir (e.g. Mulders 2003) but scales with reservoir compaction. Given the detailed
geometries of reservoir-fault intersections are largely uncertain it is reasonable to assume any variability in
these geometries is uniformly distributed throughout the reservoir. Under these circumstances, the faults
most likely to experience greatest differential compaction are those located in the region of greatest
reservoir compaction. Consequently, without additional detailed information about the distribution and
geometry of reservoir-fault intersections, the differential compaction model is equivalent to the earlier strain
model (Kostrov 1974; McGarr 1976). The upgraded earthquake monitoring network may yield better
information about the relationship between induced seismicity, mapped faults, and differential reservoir
compaction.

Figure 6.4

The locations of M ≥ 1.5 earthquakes between 1995 and 2012 in relation to reservoir pressure
depletion, reservoir compaction, and compaction gradients from 1957 to 2012.

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6.2.2

Earthquakes and gas production

Total seismic moment and gas production
The fraction of induced strain accommodated by seismogenic fault slip within the Groningen Field is central
to assessing future seismicity on the basis of future gas production and reservoir compaction. Let us call this
the fault strain partition fraction, or . The behavior of this partition fraction may be appreciated by
comparing the total observed seismic moment with that expected from the strain model as a function of
cumulative gas production since April 1995 (Figure 6.5). The observed total seismic moment at the end of
this period is about 1015 Nm. However given the change in reservoir compaction over the same period, the
total seismic moment expected if all this strain had been accommodated by seismogenic slip on faults, i.e.
= 1, is about 2 x 1018 Nm. Clearly, most of the strain was not accommodated by earthquakes and the
observed partition fraction is extremely small at  = 5 x 10-4, or 1 part in 2000. However, Figure 6.5 also
indicates that this partition factor may have increased with production over the period by a factor of 5 from
an initial value of about  = 2 x 1013/2 x 1017 = 10-4. If this trend is real and sustained then it represents
an escalation in the expected total seismic moment released per unit gas production.
Due to the stochastic nature of earthquakes, the observed total seismic moment of 187 events with a bvalue of 1 might equally have taken a different value. The probability distribution of these different values is
a Pareto sum distribution (Zaliapin et al. 2005). A notable feature of this distribution is the extremely
skewed distribution of confidence bounds around the median, for example the 95% confidence interval
shown in Figure 6.5 extends from just below the median to a factor of 100 above. Consequently this
apparent trend of increasing total seismic moment per unit gas production is not yet statistically significant
as a strain model with constant partitioning ( = 5 x 10-4) does not yet fall outside the 95% confidence
interval. Nonetheless, it is appropriate to be conservative about this uncertainty and to recognize the
possibility that the partitioning factor may continue increasing with production.

Figure 6.5

Total observed seismic moment from M≥1.5 events since April 1995 as a function of total gas
production since April 1995. The observed trend suggests the expected total seismic moment per unit
production increased. However, given the limited duration of this trend relative to the uncertainty, the
significance of this trend is not yet clear.

Activity rate and cumulative production
The number of M>1.5 events within a given interval of gas production, known as the activity rate, appears
to have increased with cumulative production (Figure 6.6). A constant activity rate would appear as a
straight line, but the data clearly indicate an increasing slope with cumulative production suggesting an
increasing activity rate with cumulative production. However, the statistical significance of this apparent
trend needs careful consideration. Due to the stochastic nature of earthquakes the appropriate statistical test
is the Kolmogorov-Smirnov test for a homogeneous Poisson point process. This test is represented in Figure
6.6 by the 95% and 99% confidence intervals (solid grey lines) around the constant activity rate model
(dashed grey line). If the data are not contained within these confidence bound then the model may be
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rejected with that same confidence level. Figure 6.6 a clearly shows the data continuously outside the 99%
confidence bound over the interval of normalized production from 0.3 to 0.6.
However, this analysis is based on all the events being statistically independent of each other. One
standard test for event independence is that the distribution of waiting times between events should reflect
random chance. However, the observed distribution of waiting times indicates an over-abundance of
waiting times of less than three days (Bourne & Oates 2013). Repeating the Kolmogorov-Smirnov test after
excluding these potentially correlated events yields a marginal result (Figure 6.6 b). The possible interdependence between some events clearly confuses the assessment of activity rates escalating with
cumulative production.
In summary, the number of M≥1.5 events per unit gas production increased with cumulative production, but
the number of independent events per unit gas production may have remained constant.

(a)
Figure 6.6

(b)
The evidence for any increase in the number of earthquakes per unit gas production is weak due to
uncertainty about event clustering. (a) The observed cumulative distribution of M≥1.5 event numbers
with respect to production. The dashed grey line denotes the homogeneous Poisson process model, i.e.
the expected number of events per unit production is constant. Solid grey lines denote the 95% and 99%
confidence interval for this model according to the Kolmogorov-Smirnov test statistic. (b) As (a) except
excluding events within 3 days of the previous event that are likely not independent.

Activity rate and production rate
The The volume of gas produced between successive events, the inter-event gas volume, is simply the
inverse of the activity rate. The inter-event gas volumes measured for the M ≥ 1.5 events from April 1995 to
October 2012 when plotted against the rate at which those gas volumes were produced (Figure 6.7) do not
show significant sensitivity to the historic variation in production rate, even allowing for the possibility of a
12 month delay between gas production and earthquake occurrence.

(a)

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(b)
Figure 6.7

Inter-event gas production exhibits little sensitivity to the rate of gas production. To avoid any bias of
event clustering, all events within 3 days of the previous event were excluded. Dots and grey bars
denote the average and the range of inter-event production rates respectively; in some instances the
grey bar is so small as to be covered by the black dot; (a) and (b) represent 0- and 12-months lag
times, respectively.

Muntendam-Bos & de Waal (2013) used the same historic data to show one instance of a fit to these data
with a preliminary model for the activity rate based on the cumulative gas production and the annual rate
of gas production with a delay of a year. Though the authors did not assess uncertainties in the estimated
model parameters, they did conclude that if production rates were lowered to12 bcm/year there would be
“almost no earthquakes with magnitudes ≥1.5 would occur after a number of years”. Figure 6.7 shows the
actual variability in the historic data and so offers a simple means of assessing the validity of this
conclusion. For historic gas production rates of about 12 bcm/year (1 bcm/month), Figure 6.7 indicates
historic inter-event volumes of gas production in the range of 0.1 to 1 bcm. This is equivalent to
instantaneous activity rates of 12 to 120 M≥1.5 events per year which clearly contradicts the conclusion of
almost no earthquakes. This contradiction arises because Muntendam-Bos & de Waal did not assess
uncertainty in their estimated model parameters despite the large observed variability between activity rates
and production rates.
6.2.3

Earthquakes and compaction

Seismic moment and compaction
Let us now consider how the strain partitioning factor, , may vary with reservoir compaction. To measure
this effect, each M ≥ 1.5 event between April 1995 and October 2012 was compared to the 2012
reservoir compaction map (Figure 6.4). This compaction map was divided into discrete regions bounded
by a set of compaction contours. Within each region, the total observed seismic moment may then be
compared to the reservoir moment (Figure 6.8a). The reservoir moment is simply the maximum seismic
moment if all strain is accommodated by slip on faults, i.e.  = 1. The fraction of the total seismic moment
to the reservoir moment is then a measure of the partitioning factor for each region that corresponds to a
narrow range of reservoir compaction values. These results (Figure 6.8b) clearly indicate the partitioning
fraction increases significantly with compaction in an approximately log-linear fashion.
Doubling the reservoir compaction from 0.15 m to 0.3 m increased the partitioning fraction by a factor of
100. An exponential functional form was therefore chosen for the strain partitioning model. The uncertainty
bounds on the data follow Pareto sum distributions that were approximated by a log-normal distribution
where the standard deviation was set equal to the approximation for Pareto sums, as obtained by Zaliapin
et al. (2005). This reduced model parameter estimation to a standard least-squares regression. The
maximum likelihood model is shown in Figure 6.8b. Uncertainties in this model were estimated using the
Monte Carlo bootstrap sample-resample method to obtain the probability distribution of all models
consistent with the data. The 95% confidence interval for this distribution of models is indicated in Figure
6.8(b).

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(a)

(b)

Figure 6.8

(a) The distribution of reservoir moment and earthquake moment with reservoir compaction.
(b) The ratio of earthquake moment to reservoir moment (total seismic moment fraction) significantly
increases with reservoir compaction. An exponential model fit to the observed total seismic moment
fractions (solid line) and the associated 68% and 95% confidence intervals found by Monte Carlo
analysis.

Activity rate and compaction
Repeating the process described in the previous section but for the number of events and the reservoir
volume reduction similarly indicates a significant increase in the number of events per unit reservoir volume
reduction (Figure 6.9).

(a)

(b)

Figure 6.9

6.2.4

(a) The distribution of reservoir volume change and M ≥ 1.5 event numbers with reservoir compaction.
(b) The number of events per unit reservoir volume change significantly increases with reservoir
compaction.

Seismological model

A seismological model is required to explain the observed seismicity and provide a framework for
assessing future seismicity. Such a model cannot and will not predict individual earthquakes but it should
provide a reliable means of characterizing the statistical properties of future earthquakes; such as the
probability of exceeding a given earthquake magnitude over some given time interval. The seismological
model developed for this purpose is based on the concept of induced earthquakes accommodating induced
strain. Information about the induced strain field is available within the existing linear elastic
geomechanical model. This geomechanical model for reservoir compaction is based on past and planned
future gas production and calibrated to match the observed subsidence measurements. Information about
the fraction of this strain released as earthquakes is based on a statistical model for fault strain partitioning
as a function of reservoir compaction calibrated to the observed relationship between earthquakes and
reservoir compaction between 1995 and 2012.

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For a given time interval the median total seismic moment is simply the product of the incremental volume
change per unit area and the median strain partition factor integrated over the entire reservoir. Repeating
this process over the full statistical distribution of partitioning models yields a probability distribution for
total seismic moment (Figure 6.10).
Repeating this entire process for the set of multiple time intervals that all begin at the start of production and
end on one of the subsequent years up until the end of production, yields a model for the expected total
seismic moment as a function of past and future gas production (Figure 6.11). This seismological model
was constructed based on the map distribution of earthquakes and reservoir compaction in 2012. By
means of cross-validation, the model-based prediction for the cumulative total seismic moment with
cumulative gas production should agree with the observed cumulative total seismic moment between 1995
and 2012. The agreement between the two shown in Figure 6.11 indicates that the seismological model
provides a good description of both the areal and temporal development of seismicity in the Groningen
Field. Note that the uncertainty range around the seismic moment and the magnitude is not constant but
instead grows significantly in the future. Further data gathering is required to shift this growth of the
uncertainty further towards the future.
Based on this seismological model, Figure 6.12 shows the probability of exceeding a given earthquake
magnitude over the 10-year interval from 2013 to 2023 and over the expected remaining lifetime of
production. These estimates are contingent on the validity of the compaction model, gas production
continuing according to the current production plan and the historic fault strain partitioning trend being a
guide to the future fault strain partitioning. In this context it is important to note that alternative
geomechanical models do exist and are also calibrated to the historic subsidence but differ in their
predictions of future reservoir compaction. These represent epistemic uncertainties about future reservoir
compaction. Also, the seismological model represents the case of no time delay between reservoir
compaction and the associated induced earthquakes. This is consistent with the limited evidence available
so far for little or no time delay between changes in production rate and changes in earthquake activity
rates (Bourne & Oates 2013). However given the uncertainties associated with the limited number of
historic earthquakes it remains possible that new information will reveal the need revise the seismological
model to include a distribution of earthquake waiting times.

Figure 6.10

The cumulative probability distribution of total seismic moment predicted for the 10-year period from
2013 to 2023 and for the remaining lifetime of production based on the current production plan and
the current reservoir compaction model.

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Figure 6.11

Model for the expected total seismic moment released as a function of cumulative gas production since
the start of gas production. Dashed lines denote the 95% confidence interval for this model. This model
is only applicable if the past and future seismicity follow the same trend as the observed seismicity
between 1995 and 2012. The grey line denotes the maximum magnitude if all the previously
accumulated reservoir moment were to be released in a single event at that moment. Note that the left
axis denotes the cumulative total seismic moment of all earthquakes, whereas the right axis is the
magnitude of a single event that releases the same seismic moment.

Figure 6.12

Probability of exceeding a given magnitude over the period up to 2023 and over the remaining lifetime
of gas production. These are contingent on future induced earthquakes following the same trend of
increasing fault strain partitioning observed within the historic earthquake catalogue and a constant bvalue of 1.

6.3

Potential influence of Time-Dependent and Rate-dependent Effects on future Seismicity

The seismological model used to explain historic seismicity and to predict future seismic hazards depends
on reservoir compaction. Gas production causes reservoir pore pressure depletion. Pressure depletion
causes reservoir compaction. Reservoir compaction induces strains in and around the reservoir. Most of this
strain is accommodated by elastic deformations. As gas production continues, these induced strains
increase and so do the mechanical loads acting on pre-existing faults located in and around the reservoir.
On occasion these loads exceed the local frictional strength of a particular fault segment. This fault segment
will then slip, relaxing the mechanical load acting on the fault until frictional resistance prevents further slip.
This process transfers strain from the surrounding medium and localises it along the fault.
Once initiated, fault slip may occur steadily and slowly in a stable creeping process that generates no
earthquakes. Alternative, fault slip may occur abruptly with the fault then stuck until the cycle repeats. This
stick-slip behaviour generates an earthquake with every cycle of fault slip. The amount and area of abrupt
slip on the fault determines the magnitude of the earthquake. By slipping, a population of faults may
accommodate some of the induced strains. Induced seismicity depends on the amount of induced strain, the
fraction of induced strain accommodated by fault slip, and the fraction of fault slip that is seismogenic. All
three factors are likely increasing with the ongoing gas production. Alternative production plans may
reduce future seismicity relative to the current production plan by reducing the rate of increase in one or
more of these three factors.

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The influence of different future production rate scenarios on the seismic hazard over the next 3, 5 and 10
years is described in Section 8.9. Their influence depends on time-dependent and rate-dependent effects.
Some of these effects are included in the seismic hazard assessment based on evidence from the observed
historic behaviour of the Groningen Field. Others are currently excluded given the absence of evidence
from past field behaviour, or the absence of any influence on the hazard during model sensitivity tests.
6.3.1

Time-dependent effects

Three possible time-delays exist between gas production and induced seismicity. First, there is a time-delay
between gas production at the wells and pressure depletion within the area of seismicity due to the time
required for pressure diffusion within the gas reservoir. This is well-measured by reservoir pressure gauges
over the production history of the field and well-modelled by the current reservoir simulation model.
Currently, this represents a time-delay of about 3-5 years between any reduction in production rates from
the southern well clusters and a corresponding reduction in the rate of pressure depletion. This time-delay is
included in the seismic hazard assessment.
Second, there is a potential time-delay between reservoir pressure depletion and reservoir compaction due
to wide range of potential non-equilibrium processes such as compaction creep. This time-delay should be
evident as a delay between reservoir depletion and surface subsidence. Analysis of these data indicates a
characteristic time-delay between depletion and compaction of 0 to 10 years. The linear and bi-linear
reservoir compaction models included in the hazard assessment represent the case of a zero time-delay.
The time-decay reservoir compaction model included in the hazard assessment represents the case of a 5year characteristic delay. Further studies are ongoing to investigate time-dependent compaction behaviour.
Third, there is a possibility of a time-delay between reservoir compaction and induced seismicity. This is
currently excluded from the hazard assessment because a zero time-delay model is sufficient to explain the
historic seismicity as a function of compaction. Nonetheless, there remains a possibility of some continued
seismicity after reservoir compaction ends.
6.3.2

Rate-dependent effects

Lower strain rates associated with lower gas production rates might, in principle, offer opportunities to
lower the seismic hazard in one of three ways. First, lower strain rates might also lower the induced strain
per unit depletion. There is some experimental evidence for this effect from rate-dependent compaction tests
on sandstone reservoir rocks. This possibility is represented by two different varieties of Isotach reservoir
compaction models that are included in the seismic hazard assessment.
Second, lower strain rates might lower the fraction of strain accommodated by slip on faults. Lower strain
rates might lower the loads acting on faults, thereby reducing the number of slipping faults. Third, lower
strain rates might lower the fraction of seismogenic slip on faults. If either of these two possible effects were
significant during the period of observed historic seismicity then some variation in seismicity with varying
production rate might be present.
Using these data, Muntendam-Bos & Waal (2013) show one instance of a fit with a preliminary ratedependent model for the activity rate based on the cumulative gas production and the annual rate of gas
production with a delay of a year. Though the authors did not assess uncertainties in the estimated model
parameters, they did conclude that if production rates were lowered to 12 bcm/year there would be
“almost no earthquakes with magnitudes ≥1.5 would occur after a number of years”. Figure 6.7 shows that
at this production rate M≥1.5 events occurred at instantaneous activity rates of 12 to 120 per year which
clearly contradicts the conclusion of almost no earthquakes. Given this absence of field evidence these
mechanisms are not currently included in the hazard assessment.
Some experimental data do appear to suggest the possibility of rate-dependent effects that might limit stress
development in granular materials (e.g. Hartley & Behringer 2003). However, these results are more
applicable to loose sands rather than sandstones and the measured effects were for strain rates
substantially greater than those within the Groningen reservoir. Lahaie & Grasso (1999) analysis seismicity
induced by gas production from the Lacq reservoir in France to conclude the b-value depends on the rate
of gas production. They conclude that lower production rates might be beneficial as they yield higher bvalues meaning the relative contribution of larger magnitude earthquakes to the total seismicity is less. This
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possible benefit was assessed for the Groningen field by testing the influence of b-value on the seismic
hazard. The results indicate even substantial variability in b-value has almost no influence on the seismic
hazard. This is due to stochastic variability in the ground motion prediction equation counter-acting the
variability in seismicity due to changes in the b-value.

6.4










6.5

Conclusions
According to current model-based predictions for the areal distribution of reservoir compaction at the
end of field life, an induced earthquake of M>6.5 is physically impossible. This limit, however,
provides no information about the likelihood of an induced M ≤ 6.5 earthquake.
The location, frequency and magnitude of observed earthquakes in the Groningen field are consistent
with bulk reservoir volume changes (compaction), being the main source of seismicity.
The fraction of bulk reservoir volume changes accommodated by seismogenic fault slip is about 0.1%.
If this fraction were invariant with production, the maximum magnitude of a future event is expected to
be M=4.5 with a 95% upper bound of M=5.5.
There is evidence that the release of seismic moment per unit production escalated with increasing
production volume. If this trend continues with future production, an increasingly large fraction of the
induced strain may be accommodated by seismogenic fault slip.
The fraction of induced strain accommodated by earthquakes is likely increasing with increasing
reservoir compaction. This explains the observed temporal and areal distribution of earthquakes.
If this escalation trend continues with future production the footprint of earthquakes is expected to
continue extending from the centre towards the edges of the field, but the relative abundance of
earthquakes will continue to be localized within the regions of largest reservoir compaction in the
centre of the field.
If future seismicity follows the same trend of increasing fault strain partitioning observed in the historic
earthquake catalogue, then based on the current analysis there is a 1 in 2 chance of M>4.2, a 1 in 10
chance of M>4.9, and a 1 in 100 chance of M>5.4 before the end of 2023. These estimates include
known uncertainties associated with the trend of seismogenic fault slip accommodating an increasing
fraction of the induced strain but currently exclude the possible trend of the b-value decreasing with
compaction that may increase the likelihoods stated. These estimates also exclude the possibility of
triggered earthquakes that may increase these likelihoods.

Recommendations for further work

The recommendations below will address the main remaining uncertainties and aim to provide a better
description of the seismicity and hazard.



Use the improved earthquake monitoring results expected to become available from the enhanced
earthquake monitoring network to characterize the depth distribution of earthquakes and their spatial
relationship to mapped faults subjected to differential compaction. If appropriate, update the
seismological model according to this new information.



Assess epistemic and aleatory uncertainty associated with the reservoir compaction model and include
this in the estimates for the fault strain partition and future total seismic moment release.



Complete a full 3D poro-elastic simulation of the geomechanical response of the field calibrated by the
historical subsidence, gas production, and reservoir pressure depletion data.



Investigate different options for obtaining a robust bias-free estimate for fault strain partitioning based
on historic seismicity and compaction to validate or improve the current method. Consider extending
the strain partitioning analysis to explicitly allow for differential compaction inducing slip on specific
known faults and also the possibility of anelastic strains within the reservoir.



Re-assess the characterization of correlated earthquakes to ensure these do not bias the hazard
assessment.



Further investigate the possibility of any weak but statistically significant evidence for the effect of gas
production rate on the lifetime earthquake hazard.

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


6.6

Further investigate evidence for any statistically significant time delay between pressure depletion and
reservoir compaction and between reservoir compaction and seismicity.
Re-assess the seismological model at regular intervals in relation to future earthquakes and subsidence.
Assess the continuing validity of model assumptions and the influence of uncertainties on this
seismological model.

References

Bourne, S J & Oates, S., 2013. Probability of an earthquake greater than magnitude 4 due to gas
production from the Groningen Field, Rijswijk, The Netherlands.
Bourne, S.J. et al., 2006. Monitoring deformation of a carbonate field in Oman: Evidence for large-scale
fault reactivation from microseismic, InSAR & GPS. In 68th EAGE Conference and Exhibition, Vienna.
Bourne, S.J. & Oates, S., 2013. Induced strain and induced earthquakes within the Groningen Gas Field:
Earthquake probability estimates associated with future gas production, Rijswijk, The Netherlands.
Crook, de T., Dost, B & Haak, H.W., 1995. Analyse van het seismische risico in Noord-Nederland,
Crook, de T., Haak, H.W. & Dost, B, 1998. Seismisch risico in noord-Nederland Technical., Koninklijk
Nederlands Meteorologisch.
Dost, Bernard et al., 2012. Monitoring induced seismicity in the North of the Netherlands: status report
2010,
Dost, Bernard & Kraaijpoel, D., 2013. The August 16 , 2012 earthquake near Huizinge (Groningen), de
Bilt, The Netherlands.
Eck, van T. et al., 2004. Seismic hazard due to small shallow induced earthquakes WR 2004-01.,
Koninklijk Nederlands Meteorolgisch.
van Eck, Torild et al., 2006. Seismic hazard due to small-magnitude, shallow-source, induced earthquakes
in The Netherlands. Engineering Geology, 87(1-2), pp.105–121. Available at:
http://linkinghub.elsevier.com/retrieve/pii/S0013795206001803 [Accessed November 6, 2012].
Gutenberg, B. & Richter, C., 1954. Seismicity of the Earth and Associated Phenomena 2nd editio.,
Princeton University Press, Princeton, New Jersey.
Hager, B.H. & Toksöz, M.N., 2009. Technical Review of the Bergermeer Seismicity Study, TNO Report
2008-U-R1071/B, Cambridge, MA, USA.
Hanks, T.C. & Kanamori, H., 1979. Moment magnitude scale. Journal of Geophysical Research, 84(B5),
pp.2348–2350.
Hartley, R.R. & Behringer, R.P., 2003. Logarithmic rate dependence of force networks in sheared granular
materials. Nature, 421(6926), pp.928–31. Available at:
http://www.ncbi.nlm.nih.gov/pubmed/12606996.
Klose, C.D., 2012. Mechanical and statistical evidence of the causality of human-made mass shifts on the
Earth’s upper crust and the occurrence of earthquakes. Journal of Seismology, 17, pp.109–135. Available
at: http://www.springerlink.com/index/10.1007/s10950-012-9321-8 [Accessed November 13, 2012].
Kostrov, V.V., 1974. Seismic moment and energy of earthquakes, and seismic flow of rocks. Izv. Acad. Sci.
USSR Phys. Solid Earth, Eng. Transl., 1, pp.23–40.
Kraaijpoel, D. et al., 2008. Location of induced earthquakes in the Netherlands gas fields. In ESC General
Assembly 2008, September 7-12, 2008, Hersonissos.
Lahaie, F. & Grasso, J.R., 1999. Loading rate impact on fracturing pattern: Lessons from hydrocarbon
recovery, Lacq Gas Field, France. Journal of Geophysical Research, 104(B8), p.17941. Available at:
http://doi.wiley.com/10.1029/1999JB900139.

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Leonard, M. (2010). Earthquake Fault Scaling: Self-Consistent Relating of Rupture Length, Width, Average
Displacement, and Moment Release. Bulletin of the Seismological Society of America, 100(5A), 1971–
1988. doi:10.1785/0120090189
Maury, V.M.R., Grassob, J.-R. & Wittlinger, G., 1992. Monitoring of subsidence and induced seismicity in
the Lacq Gas Field (France): the consequences on gas production and field operation. Engineering
Geology, 32(3), pp.123–135. Available at:
http://linkinghub.elsevier.com/retrieve/pii/001379529290041V.
McGarr, A., 1976. Seismic Moments and Volume Changes. Journal of Geophysical Research, 81(8),
pp.1487–1494.
Muntendam-Bos, A.G. et al., 2008. Bergermeer Seismicity Study, TNO report 2008-U-R1071/B.
Muntendam-Bos, A. G., & Waal, J. A. de. (2013). Reassessment of the probability of higher magnitude
earthquakes in the Groningen gas field: Including a position statement by KNMI. Retrieved from
http://www.sodm.nl/sites/default/files/redactie/20130116 groningen seismicity report final.pdf
Segall, P., 1989. Earthquakes triggered by fluid extraction. Geology, 17(October), pp.942–946.
Segall, Paul, Grasso, J.-R. & Mossop, A., 1994. Poroelastic stressing and induced seismicity near the Lacq
gas field, southwestern France. Journal of Geophysical Research, 99(B8), pp.15,423–15,438.
Wassing, B B T, Van Eck, T & Van Eijs, R.M.H.E., 2004. Seismisch hazard van geïnduceerde
aardbevingen Integratie van deelstudies, Utrecht: TNO.
Zaliapin, I.V., Kagan, Y.Y. & Schoenberg, F.P., 2005. Approximating the Distribution of Pareto Sums. Pure
and Applied Geophysics, 162(6-7), pp.1187–1228. Available at:
http://www.springerlink.com/index/10.1007/s00024-004-2666-3 [Accessed November 16, 2012].
Zoback, M. & Zinke, J., 2002. Production-induced Normal Faulting in the Valhall and Ekofisk Oil Fields.
Pure and Applied Geophysics, 159, pp.403–420.

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7 Movement at surface
7.1

Ground-Motion Prediction Equations (GMPEs)

Earthquake hazard is sometimes discussed in terms of magnitude, which only defines the amount of seismic
energy released at the source of an earthquake. To characterize the hazard in a manner that is relevant to
the potential impact of the earthquake on the built environment (houses, infrastructure, etc.), it is necessary
to quantify the hazard in terms of the nature of the ground shaking produced at any given surface location.
Strong shaking produced by earthquakes is recorded on accelerographs, which register the particle
acceleration experienced at the instrument location as a function of time. There are many different
parameters that are used to characterize the shaking recorded in such accelerograms, each of which
conveys only some features of the ground motion. The simplest and most widely-used of these parameters is
the maximum amplitude on the acceleration time series, the peak ground acceleration (PGA). Integration of
the accelerogram over time yields the velocity time-series, the maximum amplitude of which is another
commonly used parameter, PGV (peak ground velocity). Both of these parameters are illustrated in Figure
7.1.

Figure 7.1

Example of acceleration and velocity time-series of strong earthquake shaking, with PGA and PGV
indicated (not based on Groningen)

In order to estimate the expected values of parameters such as PGA and PGV at a given location as a result
of a specific earthquake scenario, equations are required that relate the parameter of interest to variables
that characterize the earthquake, its location with respect to the site of interest, and the nature of the site
itself. The earthquake itself is generally characterized by its magnitude, M, and the style-of-faulting, SoF
(i.e., normal, reverse or strike-slip rupture); the relative locations of the earthquake and the site by the
distance, R, that separates them; and the site itself by a classification of its stiffness, most commonly now
using the average shear-wave velocity over the uppermost 30 meters, Vs30. Using larger collections of
ground-motion recordings, empirical equations are obtained fitting a suitable functional form, relating PGA
or PGV to the variables listed above. Such equations have often been referred to as ‘attenuation
relationships’ but this is now considered a misnomer since they describe both how the peak motions decay
with distance and how they scale with magnitude and other parameters. For this reason, these relationships
are now generally called ground-motion prediction equations, or GMPEs.
A very important feature to note with such equations is that the data always show a large scatter around
the best fit obtained through regression analyses. Residuals are calculated as the difference between

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recorded and predicted values of PGA or PGV, and the distribution of these residuals represents the scatter
in the predictions. This scatter exists for several reasons, including the influence of variables that are not
included in the equations (e.g., stress drop, fault rupture directivity effects, azimuthal variations in energy
propagation from the source, differences in the deeper geological structure) and the fact that the
explanatory variables employed, such as Vs30, can yield identical values for very different sites with
markedly distinct dynamic response characteristics (e.g., Papaspiliou et al., 2012). Several studies have
shown that when the equations are derived in terms of the logarithmic value of PGA or PGV, the residuals
conform to a standard normal (Gaussian) distribution, which is fully characterized by the standard
deviation,  (sigma).
The consequence of the scatter in the residuals is that GMPEs cannot be considered to predict unique values
of PGA or PGV for a given combination of magnitude, style-of-faulting, distance and site classification, but
rather a probabilistic distribution of values of peak acceleration or velocity. The general form of a GMPE is
therefore expressed as:

log( PGA)  f (M , SoF , R,Vs 30 )  

(7.1)

In this expression, the term  is simply the normalized residual, or in other words the number of logarithmic
standard deviations above or below the median (or logarithmic mean) value of the predicted peak motion.
Figure 7.2 illustrates these concepts with examples of a GMPE from California and recorded PGA values
from a single earthquake in that region.
If the scatter in the distribution of residuals is used when GMPEs are deployed to estimate PGA and PGV
values, the median values of the peak motion that will be obtained will have a 50% chance of being
exceeded at each location in the event of the earthquake scenario taking place. If calculations of the impact
of a hypothetical earthquake scenario (in terms of damage to buildings, for example, through the use of
fragility functions relating expected levels of damage in different building types to PGA or PGV) are made
using only predicted median levels of motion, the result will be severe underestimation. The reason for this
is that the lognormal distribution is strongly asymmetrical hence correct sampling of the full distribution
would produce several very high values of PGA or PGV, leading to greater damage.

Figure 7.2

PGA values (blue triangles) recorded on soil sites during the magnitude 6.0 earthquake that occurred in
Parkfield, California, in September 2004, plotted as a function of distance, and compared with
predictions from the Californian GMPE of Boore et al. (1997). The predicted PGA values are shown for

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the median values and for one and two standard deviation above and below the logarithmic mean.
Figure adapted from Bommer & Abrahamson (2006).

In passing, it is worth noting that in regions of less frequent seismic activity, where there are generally very
few, if any, recordings of stronger shaking from moderate-to-large earthquakes, it is common practice to
analyse recordings of smaller earthquakes to infer stochastic source, path and site parameters that can be
related to the Fourier spectra of the motions. These parameter suites can then be used to simulate ground
motions from larger earthquakes (e.g., Boore, 2003). Such simulations can then be used to either directly
derive so-called stochastic GMPEs (e.g., Rietbrock et al., 2013) or to apply adjustments to empirical GMPEs
from other regions to render them more appropriate to the target region for their application (e.g.,
Campbell, 2003). Although such approaches provide a convenient option for developing GMPEs in the
absence of extensive strong-motion databases, their use is not straightforward.
The most fundamental challenge in using the stochastic approach is that there are significant trade-offs
between the different source, path and site parameters obtained through the inversions, with the result that
there are many combinations of these parameters that are consistent with the Fourier amplitude spectra of
the recordings from small-magnitude earthquakes. The problem then arises when these suites of parameters
are used to estimate PGA and PGV from larger earthquakes since the different parameter values can lead
to widely divergent results. Additionally, for certain parameters, such as the stress drop associated with the
fault rupture, assumptions need to be made regarding whether or not the value should remain constant
over all magnitude, an issue on which there are divergent views within the seismological community.
Moreover, without running very large numbers of simulations that sample the full ranges of all the
stochastic source, path and site parameters (considering their correlations), it is difficult to obtain
meaningful estimates of the aleatory variability associated with stochastic GMPEs. In view of these
challenges, and the absence of an available study performing stochastic inversions for weak motions in the
Netherlands, the focus in this application is entirely on the use of empirical GMPEs.

7.2

Development of GMPEs for Groningen

This section describes the development of appropriate GMPEs for use in the seismic hazard and risk
assessments for the Groningen gas field. The uncertainty associated with the proposed models, as well as
some important aspects of characterizing the ground shaking hazard that are not covered by these
equations but which need careful consideration, are also briefly discussed.
7.2.1

Criteria for Groningen GMPEs

The first step was to establish the requirements for the ground-motion prediction equations for application
to the assessment of seismic hazard (probability of different levels of ground shaking) and seismic risk
(probability of different degrees of consequences in terms of damage to buildings) due to induced
earthquakes in the Groningen gas field. These requirements are as follows:
1. Compatible equations to predict both PGA and PGV. This is required because many of the available
fragility functions that could be adopted or adapted for the risk analysis are characterized in terms of
PGA (e.g., Bothara et al., 2010), whilst PGV is widely recognized to be a more robust indicator of the
damage potential of ground shaking by virtue of its relation to the energy content of the motion (e.g.,
Bommer & Alarcón, 2006). PGV has also been found to perform better than PGA as a basis for
estimating Modified Mercalli intensities from ground-motion parameters (Wald et al., 1999; Dangkua &
Cramer, 2011; Worden et al., 2012).
2. The equations need to be applied to earthquakes from magnitude Mw 1.5 to Mw 6.5, which is the range
considered in the probabilistic seismic hazard analysis (PSHA). This is an important consideration since
most current GMPEs are derived for application to earthquakes of M w 5 and greater. Several recent
studies have shown that when such equations are extrapolated for application to smaller earthquakes,
they lead to gross overestimation of the ground motions (Bommer et al., 2007; Atkinson & Morrison,
2009; Chiou et al., 2010).
3. Since the individual earthquake events in the seismicity model are represented as points (hypocenters,
which are defined by an epicentral location and a constant focal depth of 3 km), it is important to use a
GMPE that is also based either on hypocentral distance, Rhyp, or else on epicentral distance, Repi, but
derived from data with a similarly shallow depth distribution. This criterion is important because most
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modern GMPEs use distances measured relative to the finite fault rupture, but employing such equations
in PSHA in which earthquakes are represented as hypocenters will lead to underestimation of the
hazard contributions from larger magnitude earthquakes (Bommer & Akkar, 2012).
4. The ground surface conditions in much of the Groningen field have been classified as being soft soils,
typically with a time-average shear-wave velocity over the top 30 meters, Vs30, on the order of 200
m/s. The GMPE therefore needs to be calibrated to such soft soil sites or include V s30 as an explicit
predictor variable that can take such a small value.
5. As well as reporting the standard deviation (sigma) to characterize the distribution of logarithmic
residuals, the decomposition of sigma into between-earthquake and between-station components is
required. The first component represents how different is the average level of shaking from average
earthquakes of the same magnitude and style-of-faulting (which may reflect, for example, the stress
drop), whereas the second represents how the motion at individual locations during one earthquake
differ from average predictions for sites on similar geology at the same distances (which is effectively
the spatial variability of the motion). When calculations are performed using Monte Carlo simulations to
estimate ground shaking at many locations and the consequent impact on the built environment, it is
necessary to correctly sample these two components of variability for each earthquake and then for all
the sites at which the shaking is calculated (see Crowley & Bommer, 2006).
Ideally, the equation would also be applicable to the region under study, which would arise from being
derived from local recordings or else using data from a clearly analogous region.
The first criterion alone greatly reduced the number of published equations under consideration since
although there are many GMPEs available for PGA (Douglas, 2011), the number that also provide PGV
predictions is considerably smaller (Douglas, 2012). None of the equations in contention automatically
satisfy all of the other requirements, so the search focused instead of identifying GMPEs which satisfied
some of these criteria and for which it would be relatively straightforward to make adjustments for the noncompliance with other criteria. This excludes, for example, the equations of Douglas et al. (2013) derived
from recordings of induced earthquakes from geothermal fields and other areas worldwide, since although
they satisfy several of the requirements, they are calibrated to a unique value of V s30 (1,100 m/s). The
application of these equations to the Groningen field would therefore require site response analyses to
transform the predicted motions at a horizon within the underlying bedrock to the ground surface, which
would impose a significant computational penalty on the hazard calculations. Moreover, without extensive
and detailed field and laboratory tests to determine the dynamic characteristics of the overlying soils, such
a step could introduce considerable additional uncertainty into the estimates of surface motions.
After consideration of some 17 equations derived using data down to smaller magnitude (M w < 3.5), the
choice was made to select the equations of Dost et al. (2004) as these were derived for shallow
earthquakes in the Netherlands (section 7.2.2). Although derived in terms of local magnitude, ML, rather
than moment magnitude, Mw, in the range of application it may be assumed that the two scales are
equivalent (e.g., Deichmann, 2006). The equations are derived in terms of R hyp from recordings of shallow,
mostly induced (from the Roswinkel field), earthquakes in the Netherlands, with most of the recordings
coming from relatively soft soil sites. The two criteria that the Dost et al. (2004) equations do not fully satisfy
relate to the magnitude range and to the decomposition of sigma. The problem with the former is that the
equations only model linear scaling of PGA and PGV with magnitude, which leads to unrealistically high
motions from larger earthquakes. However, a non-linear modification to the magnitude scaling at higher
values was made using the scaling from the Rhyp-based model of Akkar et al. (2013). Similarly, a
separation of the total standard deviation into between-event and within-event sigmas was made on the
basis of ratios inferred from other GMPEs.
7.2.2

The Groningen Ground-Motion Database

In essence the Dost et al. (2004) equations for PGA and PGV were chosen on the basis of having been
derived from recordings of small-magnitude, shallow earthquakes in the Netherlands, which at face value
would make them obvious candidates that are likely to be applicable to the Groningen hazard and risk
studies. Shortly after these equations were chosen, the opportunity to test this assumption arose when 40
pairs of PGA and PGV values from recordings of 8 earthquakes with magnitude in the range 2.7 to 3.6 in
the Groningen field were made available by Bernard Dost of KNMI. Figure 7.3 shows the peak motions
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plotted against hypocentral distance to the recording station, after adjustment to a common magnitude of
Mw 3.6 using the scaling terms in Dost et al. (2004). Shown on the same plot are the median predicted
values of PGA and PGV from the Dost et al. (2004) equations for an earthquake of this size, together with
the predictions at the plus-and-minus-one-standard deviation levels.

Figure 7.3

PGA and PGV values from induced earthquakes in the Groningen field (normalized to M w 3.6) plotted
against distance and compared to the predicted values from the Dost et al. (2004) equations.

It is immediately apparent from the plots in Figure 7.3 that the Dost et al. (2004) equations over-predict the
recorded peaks, on average by a factor of about 3 (the standard deviation on the log 10 peaks in. Given
that the equation is derived from recordings of small-magnitude shallow earthquakes, this raises questions
as to what causes the surface motions in the Groningen field to be of such low amplitude. The issue of overestimation by GMPEs derived from recordings of larger magnitude earthquakes is not relevant since the
Dost et al. (2004) model is based on recordings from earthquakes of comparable magnitudes.
Another possibility that might be considered is that the Groningen earthquakes are associated with
exceptionally low stress drops, which could be supported by the observation of positive correlation between
focal depth and stress drop (e.g., EPRI, 2006). If this were the explanation invoked for the low amplitudes
of the Groningen recordings, it would be difficult to explain why a similar effect was not observed in the
Roswinkel recordings that dominate the Dost et al. (2004) database. Moreover, unpublished studies of the
Groningen earthquakes at KNMI do not indicate that the earthquakes were of particularly low stress drop
(Dr Bernard Dost, personal communication, 2013).

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Before considering other potential mechanisms that could lead to the low amplitudes of acceleration and
velocity observed in the Groningen field recordings, it may be useful to look at another characteristic of the
recordings, namely the ratios of peak velocity to peak acceleration (PGV/PGA). This ratio is considered to
be an indication of the frequency content of the ground motion (e.g., Pavel & Lungu, 2013), with higher
values of the ratio corresponding to motions dominated by lower frequencies of motion. Figure 7.4 shows
the ratios from the Groningen recordings, compared with the ratios inferred from median predicted values
of PGV and PGA from the GMPEs of Dost et al. (2004) and Akkar et al. (2013). The strong dependence of
the ratio on earthquake magnitude is immediately apparent, and is entirely consistent with the expectation
from both seismological theory and empirical data that larger earthquakes produce ground motions with
relatively lower frequency contents. The surprising feature of the plot, however, is that the ratios calculated
from the Groningen recordings are very large compared to what would be expected from earthquakes in
this magnitude range. The implication would be that the mechanism leading to diminished amplitudes of
the motion preferentially suppresses high-frequency motions. This might suggest the influence of the soft
soils at the ground surface, which include peat layers in some locations, since such deposits can effectively
filter out the higher frequency motion. However, soft soil layers that would tend to significantly reduce PGA
in this way would be expected to have a much lesser effect on PGV and more likely to result in some
amplification of the peak velocity. Pronounced diminution of both PGA and PGV might result from
extremely non-linear response of the soil layers, but this would require much more energetic motions than
those that could be generated by such small-magnitude earthquakes. Therefore, near-surface attenuation
by soft soil layers is an unlikely reason for the low amplitudes of both PGV and PGA.

Figure 7.4

PGV/PGV ratios from the Groningen field data plotted against earthquake magnitude. The curves are
ratios calculated from the GMPEs of Dost et al. (2004) and Akkar et al. (2013), the latter plotted for
Vs30 = 200 m/s.

The cause for the low amplitudes of the recorded motions in the Groningen field is most probably related to
the presence of a high-velocity layer directly above the gas reservoir (Figure 7.5). This high-velocity layer is
the so-called basal anhydrite encountered in the lowest part of the Zechstein salt formation that overlies the
gas reservoir (section 2.1.2). The influence of this layer in terms of reflecting and refracting upward
propagating seismic waves is discussed by Kraaijpoel & Dost (2013). The presence of such a strong
velocity inversion is very likely to inhibit the strength of direct wave arrivals to the ground surface and this is
therefore the most likely explanation for the low levels of recorded motion in the Groningen gas field. That
this is the most likely explanation for the over-estimation of the recorded peaks illustrated in Figure 7.3 is
supported by the fact the Roswinkel gas reservoir is located above the Zechstein formation (e.g., Duin et al.,
2006).
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The apparently larger impact of the basal anhydrite on high-frequency motions (and hence on PGA) than
on PGV would be explained by the very limited thickness of the high-velocity layer, which results in its
presence only having a notable effect on seismic energy with short wavelengths (i.e., high-frequency
motions). If this is the case, then the beneficial effect would diminish to some extent with increasing
earthquake magnitude and radiated wave fields with relatively more low-frequency content. Similarly, if the
fault ruptures for larger earthquakes initiating in the reservoir were to rupture upwards, the radiation from
the portion of the fault rupture above the basal anhydrite would clearly not be affected by the velocity
contrast resulting from this stratigraphic feature.

Figure 7.5

7.2.3

Profiles of S-wave and P-wave velocities and mass density at a location in the southern part of the
Groningen field. The gas reservoir is a little of 3 km below the ground surface, directly below the lower
high-velocity layer (Oates & Noble, 2011).

Selection and Modification of the Akkar et al. (2013) GMPE

The Dost et al. (2004) GMPE was originally chosen on the basis of having been derived from recordings of
small-magnitude, shallow-focus earthquakes in the Netherlands. In view of some of the shortcomings of the
model with regards to other selection criteria listed in Section 7.2.1, and the marked overestimation of the
peaks from the Groningen field recordings (none of which were used in the derivation of the equations), it
was decided to select another equation as an alternative.
The models of Akkar et al. (2013) were identified as satisfying nearly all of the selection criteria established
to fulfill the project requirements. The study presents completely consistent equations for both PGA and
PGV, and includes equations in which the hypocentral distance metric is used. These equations include V s30
as an explanatory variable and are applicable for a range of values that extends to below 200 m/s (and
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moreover they model the non-linearity in the site response for soft sites and stronger bedrock motions). The
equations also provide the standard deviation of the aleatory variability decomposed into between-event
and within-event components. The one criterion that the equations do not satisfy (apart from the obvious
shortcoming of not being based on data from Groningen or from the Netherlands) is that the range of
earthquake magnitudes for which they are derived (Mw 4 to 7.6) does not extend as low as required. The
lower magnitude limit was set at Mw 4 rather than the more common threshold of Mw 5 precisely to avoid
overestimations at lower magnitudes, but extrapolating the equations to M w 1.5 would carry very
considerable uncertainty. Moreover, the extrapolated predictions for the magnitude-distance combinations
of the Groningen earthquake recordings (and for normal-faulting earthquakes and Vs30=200 m/s)
overestimate the recorded PGV values as much as the Dost et al. (2004) equation and the recorded PGA
values by an even greater margin.

Figure 7.6

Residuals of PGA calculated from the Groningen field motions with respect to predictions from the
adjusted Akkar et al. (2013) equation. The residuals are calculated from the natural logarithm of the
observed value minus the natural logarithm of the predicted median value.

In order to develop a model that is simultaneously well-constrained for larger magnitudes and consistent
with the Groningen field recordings at smaller magnitudes, adjustments were made to the magnitude
scaling and magnitude-dependent attenuation terms in the Akkar et al. (2013) model such that the
equation could be fitted to the Groningen data and remain unchanged above a certain magnitude
threshold, without introducing a discontinuity between the equations for small and large magnitudes. The
coefficients for these adjustments were found by inspection rather than rigorous statistical fitting, which
means that there may be other suites of values that produce slightly better fits to the data; however, since
the adjusted equations are not being extrapolated far beyond the range of the data to which they are
adjusted, such minor differences are of no importance. The adjusted equations provide a very good fit to
the Groningen data, as the residual plots for PGA in Figure 7.6 illustrate clearly (the plots for PGV
residuals look very similar).

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The final question to be addressed in developing the modified Akkar et al. (2013) equations for hazard
and risk analyses in the Groningen field is whether it was necessary to make any modifications for the
standard deviation for the smaller-magnitude portion of the adjusted equations. A number of GMPEs have
sigma values that increase with decreasing magnitude, although it is still debated whether this is an artefact
of poorly-determined magnitude and distance values for smaller events (e.g., Strasser et al., 2009). The
decision was taken to maintain the standard deviations for PGA and PGV—and their decomposition into
between-event and within-event parts—across the full range of magnitudes, for two reasons. The first of
these is that the sigma values are already large (and possibly inflated by ‘noisy’ metadata associated with
the small-magnitude recordings in the original database). The second is that the standard deviations of the
residuals shown in Figure 7.6 are relatively small, which notwithstanding the small sample size from which
they are calculated suggests that the variability in the small-magnitude predictions is not particularly large.
7.2.4

Epistemic Uncertainty in Ground-Motion Predictions

The modified Akkar et al. (2013) equations for PGA and PGV were developed as a starting model for the
estimate of peak ground motions from potential induced-earthquakes in the Groningen field, but it is
important to acknowledge that there is considerable epistemic uncertainty associated with the application
of these equations. Epistemic uncertainty refers to the uncertainty arising from incomplete knowledge,
which is distinct from the aleatory variability associated with the ground-motion predictions. Since the
database of Groningen ground-motion recordings is relatively sparse, and may therefore represent a
biased sample, there is some degree of epistemic uncertainty even in the range of small-magnitude
predictions. Even in regions with abundant databases of ground-motion recordings, such as California, it is
common practice in seismic hazard assessment to combine several locally-derived GMPEs in a logic-tree
formulation (e.g., Bommer et al., 2005), and moreover to impose additional increments of uncertainty in
the median predictions to reflect the epistemic uncertainty that may arise from biases in the data and lack
of data in certain ranges of magnitude and distance (Petersen et al., 2008). For the prediction of groundmotion amplitudes from earthquakes of Mw 4 and larger in the Groningen field, for which there are no
data whatsoever, the epistemic uncertainty must, by definition, be even larger.
For the current hazard model, time constraints have prevented the development of suitable model for the
range of epistemic uncertainty in the ground-motion predictions, which is a non-trivial undertaking if it is to
be done properly (Bommer, 2012). However, it should be borne in mind that there is likely to be an
appreciable level of epistemic uncertainty associated with the application of the proposed preliminary
equations, and the precision with which the resulting ground motions and hazard levels can be calculated
should not be interpreted as representing accuracy.
7.2.5

Spatial Correlations in Ground Motions

The GMPEs developed for and applied in this hazard study effectively assume complete independence
between the motions at different locations as a result of a particular earthquake. In practice, analyses of
recordings from dense strong-motion recording networks have shown that the peak motions at closelyspaced instruments tend to be correlated (e.g., Boore et al., 2003). The correlation decays rapidly with
increasing separation of the stations, but can be quite high for distances of less than about 3 km.
Considering spatial correlation of ground motions in probabilistic seismic risk calculations (using, as in this
project, Monte Carlo sampling), will tend to lead to higher levels of estimated damage and loss. The reason
is that the spatial correlation will produce large pockets of elevated levels of motion, and for those
simulations where these coincide with densely-populated areas of vulnerable building stock, it can be
expected that particularly high damage levels will result (e.g., Crowley et al., 2008). In the exploratory risk
calculations run so far, spatial correlation of the ground motions has not been explicitly included, which
might ordinarily lead to some underestimation of the impact on the building stock. However, the
calculations have been performed on spatially-aggregated building stock, in which PGA values are
calculated at the centre points of a 3x3 km grid, and the same level of shaking applied to all building
locations within that square. This discretization of the exposure (building stock), which is adopted for
computational efficiency, produces a very similar effect to that which would result from explicit inclusion of
spatial correlation of ground motions.

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7.2.6

Duration of Ground Shaking

Another important feature of the ground motion is the duration of the strong shaking interval, which can
also be characterized by the effective number of cycles of motion. For structures such as unreinforced
masonry, which degrade in both strength and stiffness under repeated cycles of loading, the duration of the
shaking has an important influence on the degree of damage experienced under a given amplitude of
ground motion (e.g., Bommer et al., 2004). One approach that could be used to incorporate this feature of
the ground shaking into the seismic hazard analysis would be a vector analysis that simultaneously
estimating both PGA and duration by using a suitable expression for the correlation between the residuals
in the prediction of the two parameters. Such functions have been derived by Bradley (2011) who found a
negative correlation between peak amplitudes and duration, such that when the hazard analysis produces
relatively high values of PGA, for example, these would correspond to motions with very short durations.
Since the hazard is currently being characterized only in terms of PGA and PGV, the influence of duration
must be accounted for in any fragility functions used to estimate damage. This is especially the case if the
fragility functions adopted are based on field observations following earthquakes of larger magnitude, or
else from experiments or analyses using recordings of larger magnitude earthquakes, since the Groningen
hazard study is considering a range of magnitudes that are centered on much smaller values than those
generally involved in earthquake damage and loss estimations. The duration of ground shaking scales
strongly with magnitude and small-magnitude earthquakes will generally be expected to produce ground
motions of very short duration (e.g., Bommer et al., 2009).

7.3

Conclusions

This chapter has described how the energy by an earthquake moves to the surface and causes movement of
the ground. In the next chapter, this is combined with the insights into the induction of earthquakes
(Chapter 6) motions to provide hazard maps. The following conclusions have been drawn:










Recorded peak ground accelerations (PGA) and velocities (PGV) from induced earthquakes in the
Groningen field show remarkably low amplitudes, even compared to recordings from earthquakes of
similar magnitude in the Roswinkel field.
The most likely explanation for these low amplitudes is the effect of the high-velocity, high-density basal
anhydrite layer in the Zechstein formation immediately above the Groningen gas reservoir, since this
creates a strong negative impedance contrast.
The beneficial effect of the basal anhydrite is not assumed to hold for motions from larger earthquakes
(M>4) until verified by other investigations, and for the initial hazard analysis it is assumed that
ground-motion amplitudes will be comparable to those recorded in tectonic earthquakes.
On this basis, ground-motion prediction equations (GMPEs) derived from recordings of tectonic
earthquakes in Europe and the Middle East have been adopted for the prediction of PGA and PGV.
The equations were selected on the basis of using hypocentral distance, which is consistent with the
way earthquakes are modelled in the hazard analysis, and including a term for site amplification
effects that is able to model the influence of the soft near-surface conditions encountered in much of the
Groningen field.
The equations have been adopted in their original form for larger earthquakes, but adjusted below a
threshold magnitude (4.2 for PGA, 3.8 for PGV) in order to provide a good fit to the Groningen data
at smaller magnitude levels.
Additional work is required to refine the local applicability of the selected GMPEs to the Groningen
field and also to estimate the associated levels of epistemic uncertainty.
Since currently only PGA and PGV are being predicted in the hazard analysis, it is important to take
account of the fact that for most of the earthquakes considered in the hazard analysis, the duration of
shaking will be short as a result of the small magnitude of these earthquakes.

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7.4

References

Akkar, S., M.A. Sandıkkaya & J.J. Bommer (2013). Empirical ground-motion models for point- and
extended-source crustal earthquake scenarios in Europe and the Middle East. Bulletin of Earthquake
Engineering, in press, DOI 10.1007/s10518-013-9461-4.
Atkinson, G.M. & M. Morrison (2009). Observations on regional variability in ground-motion amplitude
for small-to-moderate magnitude earthquakes in North America. Bulletin of the Seismological Society of
America 99(4), 2393-2409.
Bommer, J.J. (2012). Challenges of building logic-trees for probabilistic seismic hazard analysis.
Earthquake Spectra 28(4), 1723-1735.
Bommer, J.J. & N.A. Abrahamson (2006). Why do modern probabilistic seismic hazard analyses lead to
increased hazard estimates? Bulletin of Seismological Society of America 96(6), 1967-1977.
Bommer, J.J. & S. Akkar (2012). Consistent source-to-site distance metrics in ground-motion prediction
equations and seismic source models for PSHA. Earthquake Spectra 28(1), 1-15.
Bommer, J.J. & J.E. Alarcón (2006). The prediction and use of peak ground velocity. Journal of Earthquake
Engineering 10(1), 1-31.
Bommer, J.J., G. Magenes, J. Hancock & P. Penazzo (2004). The influence of strong-motion duration on
the seismic response of masonry structures. Bulletin of Earthquake Engineering 2(1), 1-26.
Bommer, J.J., F. Scherbaum, H. Bungum, F. Cotton, F. Sabetta & N.A. Abrahamson (2005). On the use of
logic trees for ground-motion prediction equations in seismic hazard assessment. Bulletin of the
Seismological Society of America 95(2), 377-389.
Bommer, J.J., P.J. Stafford & J.E. Alarcón (2009). Empirical equations for the prediction of the significant,
bracketed and uniform duration of earthquake ground motion. Bulletin of the Seismological Society of
America 99(6), 3217-3233.
Bommer, J.J., P.J. Stafford, J.E. Alarcón & S. Akkar (2007). The influence of magnitude range on empirical
ground-motion prediction. Bulletin of the Seismological Society of America 97(6), 2152-2170.
Boore, D.M. (2003). Simulation of ground motion using the stochastic method. Pure and Applied
Geophysics 160(3-4), 635-676.
Boore, D.M., J.F. Gibbs, W.B. Joyner, J.C. Tinsley & D.J. Ponti (2003). Estimated ground mMotion from the
1994 Northridge, California, eEarthquake at the site of the Interstate 10 and La Cienega Boulevard bridge
collapse, west Los Angeles, California. Bulletin of Seismological Society of America 93(6), 2737–2751.
Boore, D.M., W.B. Joyner & T.E. Fumal (1997). Equations for estimating horizontal response spectra and
peak acceleration from western North American earthquakes: A summary of recent work. Seismological
Research Letters 68(1), 128–153.
Bothara, J.K., R.P. Dhakal & J.B. Mander (2010). Seismic performance of an unreinforced masonry
building: An experimental investigation. Earthquake Engineering & Structural Dynamics 39(1), pp. 45-68.
Bradley, B.A. (2011). Correlation of significant duration with amplitude and cumulative intensity measures
and its use in ground motion selection. Journal of Earthquake Engineering 15(6), 809-832.
Campbell, K.W. (2003). Prediction of strong ground motion using the hybrid empirical method and its use
in the development of ground-motion (attenuation) relations in eastern North America. Bulletin of the
Seismological Society of America 93(3), 1012-1033.
Chiou, B., R. Youngs, N. Abrahamson & K. Addo (2010). Ground-motion attenuation model for small-tomoderate shallow crustal earthquakes in California and its implications on regionalization of groundmotion prediction models. Earthquake Spectra 26(4), 907-926.
Crowley, H. & J.J. Bommer (2006). Modeling seismic hazard in earthquake loss models with spatially
distributed exposure. Bulletin of Earthquake Engineering 4(3), 249-275.

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Crowley, H., P.J. Stafford & J.J. Bommer (2008). Can earthquake loss models be validated using field
observations? Journal of Earthquake Engineering 12(7), 1078-1104.
Dangkua, D.T. & Cramer, C.H. (2011). Felt intensity versus instrumental ground motion: A difference
between California and Eastern North America? Bulletin of the Seismological Society of America 101(4),
1847-1858.
Deichmann, N. (2006). Local magnitude, a moment revisited. Bulletin of the Seismological Society of
America 96(4A), 1267-1277.
Dost, B., T. van Eck & H. Haak (2004). Scaling of peak ground acceleration and peak ground velocity
recorded in the Netherlands. Bolletino di Geofisica Teorica ed Applicata 45(3), 153-168.
Douglas, J. (2011). Ground-motion prediction equations 1964-2010. BRGM/RP-59356-FR, BRGM,
Orléans, France, 444 pp.
Douglas, J. (2012). Consistency of ground-motion predictions from the past four decades: peak ground
velocity and displacement, Arias intensity and relative significant duration. Bulletin of Earthquake
Engineering 10(5), 1339-1356.
Douglas, J., B. Edwards, V. Convertito, N. Sharma, A. Tramelli, D. Kraaijpoel, B. Mena Cabrera, N.
Maercklin & C. Troise (2013). Predicting ground motion from induced earthquakes in geothermal areas.
Bulletin of the Seismological Society of America 103(3), in press.
Duin, E.J.T., J.C. Doorenbal, R.H.B. Rijkers, J.W. Verbeek & Th.E. Wong (2006). Subsurface structure of
the Netherlands – results of recent onshore and offshore mapping. Netherlands Journal of Geosciences
85(4), 245-276.
EPRI (2006). Truncation of the lognormal distribution and value of the standard deviation for ground
motion models in the Central and Eastern United States. EPRI Report No. 1013105, Electric Power
Research Institute, Palo Alto, California, 100 pp.
Kraaijpoel, D. & B. Dost (2013). Implications of salt-related propagation and mode conversion effects on
the analysis of induced seismicity. Journal of Seismology 17(1), 95-107.
Papaspiliou, M., S. Kontoe & J.J. Bommer (2012). An exploration of incorporating site response into PSHA
– Part I: Issues related to site response analysis methods. Soil Dynamics & Earthquake Engineering 42,
302-315.
Pavel, F. & D. Lungu (2013). Correlations between frequency content indicators of strong ground motion
and PGV. Journal of Earthquake Engineering 17(4), 543-559.
Petersen, M.D. A.D. Frankel, S.C. Harmsen, C.S. Mueller, K.M. Haller, R.L. Wheeler, R.L. Wesson, Y. Zeng,
O.S. Boyd, D.M. Perkins, N. Luco, E.H. Field, C.J. Wills & K.S. Rukstales (2008). Documentation for the
2008 Update of the United States National Seismic Hazard Maps, USGS Open-File Report 2008-1128,
US Geological Survey, Reston, VA. 61 pp.
Rietbrock, A., F. Strasser & B. Edwards (2013). A stochastic earthquake ground-motion prediction model
for the United Kingdom. Bulletin of the Seismological Society of America 103(1), 57-77.
Strasser, F.O., N.A. Abrahamson & J.J. Bommer (2009). Sigma: issues, insights, and challenges.
Seismological Research Letters 80(1), 40-56.
Wald, D.J., V. Quitoriano, T.H. Heaton & H. Kanamori (1999). Relationships between peak ground
acceleration, peak ground velocity, and modified Mercalli intensity in California. Earthquake Spectra
15(3), 557-564.
Worden, C.B., Gerstenberger, M.C., Rhoades, D.A. & Wald, D.J. (2012). Probabilistic relationships
between ground-motion parameters and Modified Mercalli Intensity in California. Bulletin of the
Seismological Society of America 102(1), 204-221.

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8 Probabilistic Seismic Hazard Assessment
8.1

Interpretation of Eurocode 8 for the case of production-induced seismicity

The Eurocodes are the current technical standards for structural design in Europe, and it is now compulsory
for the 28 countries in the Eurocode zone to adopt these in place of existing national standards. Eurocode
8 specifically deals with earthquake-resistant design of structures (CEN, 2006). Each country adopting
Eurocode 8 must develop a National Annex to indicate how the code is implemented; the National Annex
for the Netherlands has not yet been produced.
Eurocode 8 is based on a limit state philosophy, specifying structural performance targets associated with
different levels of seismic loading. The fundamental requirement is for No Collapse (NC), intended to
ensure life safety; this is referred to as the Ultimate Limit State (ULS), and is associated with ground motions
having a 10% probability of exceedance during 50 years, or a return period of 475-years. The
Serviceability Limit State (SLS) targets function of structures, comfort of occupants, and appearance of
buildings, and is anchored to design ground motions with 50% probability of exceedance in 50 years, or a
return period of 95 years. The 475-year return period as the basis for defining the basic design motions
was adopted in most seismic codes around the world after being first introduced in the United States,
despite being based on a series of rather arbitrary decisions rather than any quantitative risk
considerations (Bommer & Pinho, 2006).
Eurocode 8 consists of six parts, with Part 1 defining the basic seismic actions and the other parts deal with
specific structural applications. Part 3 is entitled “Assessment and Retrofitting of Buildings”, for which three
limit states and associated return periods are specified. The narration in the code notes that the Significant
Damage (SD) Limit State, tied to ground shaking with a 475-year return period, is comparable to the NC
limit state for new buildings. In this regard, Eurocode 8 is unusual in requiring that retrofitted buildings
should achieve the same levels of seismic performance as new buildings, whereas it is widely accepted in
earthquake engineering practice around the world that lower levels of seismic capacity can be accepted for
retrofitted buildings than would be required in new buildings (e.g., Grant et al., 2007; ATC, 1978; NZSEE,
2006; NEHRP, 2009).
Notwithstanding the very conservative approach of requiring seismic retrofit to match the earthquakeresistant capacity of new buildings, the framework of Eurocode 8 was adopted in this report for the
consideration of assessing the structural capacity of buildings in the Groningen field. This requires that the
PGA and PGV values corresponding to a 475-year return period, which is equivalent to an annual
exceedance probability of 0.002, must be calculated. This is done by selecting a production period,
starting now and continuing for a specified number of years, and then calculating the levels of PGA and
PGV that would have an average 0.2% probability of being exceeded during that period. Calculating the
hazard in this way, that is, for a limited time period rather than the total hazard until the end of the field
life is based on the assumption of no time delay between reservoir compaction and any associated induced
seismicity. This is consistent with an analysis of changes in earthquake activity rates which indicated little or
no delay relative to changes in gas production rates [Bourne & Oates 2013, Appendix C].

8.2

Hazard assessment: summary of approach and method implemented

Probabilistic seismic hazard analysis (PSHA) is understood to mean the characterization and estimation of
the frequency of occurrence of stated levels of earthquake ground motion at specified locations. Estimation
of predicted levels of ground motion is necessarily a probabilistic calculation but it is also common to carry
out deterministic calculations (DSHA) to estimate the ground motions due to specific earthquake scenarios
(Dost, 2013). As Bommer and Abrahamson (2006) point out, the fundamental difference between PSHA
and DSHA is simply that PSHA integrates over all earthquake scenarios, properly weighted by probability
density functions, whereas DSHA considers the effects of individual scenarios. Motivated by these
arguments, the approach that has been followed for the analysis of the Groningen induced seismic hazard
is PSHA.
PSHA methods can be divided into two categories: the classic approach in which expected ground motions
are estimated directly from the underlying probability distributions and Monte Carlo approaches in which

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distributions of ground motions are generated for synthetic earthquake catalogues by repeated sampling of
these same probability distributions. Classic PSHA has the advantage of a significant body of literature
describing the formalism and possibly advantages of speed of calculation compared with the Monte Carlo
approach. A major disadvantage of the classic PSHA approach is that it is based on the assumption of
stationary seismicity rates, defined in terms of frequencies of events per unit time, whereas the source model
that we set out to implement is derived from a time-dependent compaction process. The Monte Carlo
approach, although less well documented in the literature, has the advantage of conceptual simplicity and
versatility which enabled the compaction-based source model (Section 6.2.4) to be incorporated. It is also
more easily integrated with building exposure and fragility data to deliver risk or loss estimates over a
large urban area (see Bommer and Crowley 2006). Initial internal discussions concluded that it was
unlikely that we would find a third party PSHA software package that would properly handle the
compaction-driven induced seismic hazard of the Groningen Field. It was decided therefore to write our
own PSHA code, tailored to the needs of Groningen and following the Monte Carlo approach for the
reasons mentioned above. To be better able to identify errors in implementation of the method, it was
decided to write two independent codes, one in Python (Stephen Bourne) and the other in C (Steve Oates).
There was minimal sharing of details between the two implementations apart from a general consensus on
the overall method. This allowed independent cross-checking of the hazard results at any stage and has
proved to be key in identifying bugs and other inconsistencies.
The Monte Carlo PSHA codes have been implemented according to the following scheme. (Bourne et al
2013). Each catalogue of earthquakes is generated by first drawing a seismic moment budget from a
distribution derived from the Groningen Field compaction model (as detailed in section 6.2.4). Event
locations are assigned by randomly sampling the expected event density map which itself is derived from
the predicted compaction map characterizing the chosen production interval. Magnitudes are assigned by
random sampling of a truncated Gutenberg-Richter type distribution where the catalogue’s total moment is
constrained to not exceed the moment budget. In this way a specified number of catalogues is generated
and, for each event, ground motions (PGA and PGV) are calculated at each of the nodes of the surface
output grid. In calculating the ground motion, the distribution of variations of the chosen ground motion
prediction equation (GMPE) about its mean is randomly sampled. Hazard curves are generated for each
surface location by counting the exceedances of specified ground motion thresholds and averaging over
the number of simulated catalogues.

8.3

Hazard maps: PGA and PGV for a 475 year return period

Guided by the interpretation of Eurocode 8 as a standard, described above, maps of ground motion
equivalent to the 475 year return period are calculated from the set of hazard curves. The PGV and PGA
hazard maps generated, using the modification of the Akkar et al (2013) GMPE, described in section 7.2
above, are shown in Figure 8.1.

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Figure 8.1

Top Row: Predicted ground motion with an average annual 0.2% chance of exceedance, for the next 10
years from 2013 to 2023. Maximum ground motions are 22 cm/s and 57% of the acceleration due to
gravity (g), located above the region of greatest reservoir compaction. The black line denotes the field
outline and the letters D to W denote Delfzijl, Eemshaven, Groningen, Hoogezand, Loppersum and
Winschoten respectively. Bottom Row: Predicted ground motion with an average 10% chance of
exceedance and 50% chance of exceedance during the next 10 years from 2013 to 2023.
In these hazard calculations the linear compaction model was used.

The process described for generating ground motion hazard maps aggregates the numbers of exceedances
at each location and so does not keep track of the combinations of event magnitude, distance and GMPE
variation giving rise to the ground motion exceedance rates. Disaggregation on the other hand is the
process of looking at the hazard in terms of the magnitude, hypocentral distance and GMPE variation of
the events giving rise to it (Bazzurro and Cornell 1999), so that questions such as `What event magnitudes
and hypocentral distances contribute most to the hazard?' can be answered in a useful way. In practice,
disaggregation of the hazard requires storing additional arrays during the computation relating the
magnitude, source-surface location distance and GMPE variation to the resulting surface ground motion.
The disaggregated hazard is illustrated in Figure 8.2 and Figure 8.3, for PGA and PGV respectively.

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Figure 8.2

Disaggregation of the PGA hazard (see text for details).

Figure 8.3

Disaggregation of the PGV hazard (see text for details).

The individual plots show: (in the first rows) the distribution of PGA occurrence rates as a function of
magnitude, distance, and GMPE variation for a single surface location directly above the region of
maximum reservoir compaction; (in the second rows) the fractional contribution to the total rate of peak
ground velocities at this same surface location corresponding to mean annual exceedance probabilities of
0.2% (thick line) and 1% (thin line), equivalent to the Eurocode 8 performance requirements for a 10%
chance over 50 and 10 years respectively.

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Referring to the magnitude disaggregation plots, notice that the hazard is dominated by the intermediate
magnitudes (e.g. M=4.5-5.0) typically occurring at smaller hypocentral distances. The largest magnitudes
are so rare that they contribute very little to the hazard for this recurrence rate. One significant
consequence of this observation is that the maximum magnitude is not an appropriate means of
characterizing the seismic hazard. Note also that disaggregation of the PGV and PGA hazards leads to
appreciably different dominant magnitudes reflecting the non-trivial, magnitude- and distance-dependent
relationship between PGV and PGA implied by the ground motion prediction equations.

8.4

Sensitivity tests

Additional simulations were run to explore sensitivity of the calculated hazard to variations in the input. The
most important of these sensitivities are summarized here.
The choice of the Ground Motion Prediction Equation (GMPE) has a major impact. Most of the simulations
described in Bourne et al (2013) were run using the GMPE of Dost et al (2004), this choice having been
motivated by the fact that this GMPE was the only one available which had been calibrated using data
from the Netherlands (but not Groningen). Subsequent analysis of strong motion data for Groningen
events, including the 2012 Huizinge event, showed however that the Dost et al (2004) and Akkar et al
(2013) GMPEs significantly overestimate surface ground motions. The modification of the Akkar et al
(2013) GMPE described previously (section 7.2.3) is now favored as it has been calibrated using the
Groningen strong motion data and is constrained by data from elsewhere at higher magnitudes.
The production period over which average annualized ground motion exceedance rates are calculated was
also shown to have a strong impact on the simulated rates. This is due to the variations in the planned
production rate as the field moves from the current peak levels of off-take to lower levels of production
towards the end of its intended life (see Figure 8.5). These lower future production rates correspond to
lower compaction rates and so lower levels of annualized seismic hazard.
The Gutenburg-Richter b-value characterizing the Groningen earthquake catalogue might be expected to
have a strong influence on the calculated hazard since the b-value, being the slope of the frequencymagnitude distribution, expresses the relative abundances of small and large events in an earthquake
catalogue. Figure 8.4 (Bourne et al 2013) shows a significant variation of the b-value with compaction
when the Groningen catalogue is divided into sub catalogues defined by compaction bins. Simulations run
with a compaction-dependent b-value of this form give however very similar results to those obtained with
a constant b-value of 1. As most of the expected event density corresponds to reservoir compaction in
excess of 0.25m this function is approximately equivalent to decreasing the b-value from b=1 to b=0.8 for
the entire earthquake population. The map of peak ground velocities with a mean annual exceedance
chance of 0.2% shows a slight increase of up to 2cm/s within the central region of greatest reservoir
compaction and a similar decrease mostly located around the southern edge of the field. It seems that the
competing influences of increasing event size but reduced event number (for the large events)
corresponding to the reduced b-value approximately balance each other out, at least in this case.

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Figure 8.4

Maximum likelihood b-value estimates for subsets of the M1:5 events since April 1995 binned
according to reservoir compaction. Vertical error bars denote the 67% confidence interval and
horizontal grey lines indicate the bin sizes.

The main conclusion to be drawn from the results of the sensitivity tests is that better constraining the GMPE
for Groningen is a top priority: this is the aim of ongoing work.

8.5

Assumptions and limitations

The most important uncertainty is the partitioning coefficient relating seismic moment budget to the
volumetric strain due to compaction. The source model relies on a fit to the values of this coupling observed
so far in order to estimate the impact of any future escalation. Ongoing work shows that there are also
significant uncertainties in the compaction model for the field and these uncertainties translate into
appreciable uncertainties in the available induced seismic moment budget.
The source model is based on the production-induced compaction with the tacit assumption that suitably
located faults, pre-existing or new, will be available for the strain to be expressed as induced seismicity. A
major fault mapping exercise over the field identified approximately 1700 faults from the 3D seismic data.
It is assumed that many more faults than this will be undetectable on the 3D data due to having too small
throws and/or extents. With thousands of faults distributed across the field the above assumption that there
will be sufficient faults available on which slip can occur does not seem too restrictive. Nevertheless, an
alternative approach to calculating seismic hazard would be to propose that induced earthquakes will
occur only on certain major faults in the field. A model of this sort would be more akin to those used in
global tectonic hazard calculations. Ongoing geomechanical work allows for evaluating the likelihood for
individual faults to slip (Section 9). However, the lack of detailed information regarding fault properties
(stress, friction coefficient etc.) will make it difficult to construct compelling source models of this type.
It is assumed that the seismic moment budget derives entirely from the reservoir compaction process. That is
we neglect any contribution from other deformation processes such as tectonics and post-glacial rebound
(see Main et al 1999) that may also contribute to the magnitude of a triggered earthquake, as discussed in
section 6.1.2. See also Houtgast’s (1992) catalogue of historical seismicity in the Netherlands.
Calculating the hazard for a limited time period rather than the total hazard until the end of the field life is
based on the assumption of no time delay between reservoir compaction and any associated induced
seismicity. This is consistent with an analysis of changes in earthquake activity rates which indicated little or
no delay relative to changes in gas production rates [Bourne & Oates 2013].
The depths of the Groningen earthquakes are in almost all cases assumed to be 3000m, that is, the
approximate reservoir depth, rather than determined from the recorded data. This is due to the fact that the
monitoring array is too sparse to properly constrain event depths over most of the field. This has potential
consequences for the hazard analysis in which the same assumed event depth has been used. If event

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depths are significantly shallower than assumed, this will lead to higher than anticipated surface ground
motions.

8.6

Time evolution of seismic hazard: projections based on planned production

The expected seismic moment release rate depends on two competing effects. First, the seismic moment
released per unit of gas production increases with reservoir compaction. Second, the rate of gas production
eventually decreases due to reservoir pressure depletion. Over the next 20 years or so, the first effect is
expected to dominate and so the annual seismic moment is expected to tend to increase year-on-year.
Thereafter, the second effect will likely dominate as the annual gas production has naturally declined to
levels well- below current production rates. Figure 8.5 shows predicted initial escalation and then the long
decline in annual seismic moment release according to the statistical geomechanics model. This includes a
wide range of uncertainty between the expected and the upper bound of the 95% confidence interval, but
the same clear temporal trend with a maximum sometime between 2025 and 2035 based on the current
production plan. Such time-dependence is intrinsic to induced seismicity that necessarily has a beginning
and an end. Eurocode 8 building standards were designed with time-independent natural seismicity in
mind, as discussed above. Adaptation to time-variant induced seismicity needs to be addressed.

Figure 8.5

8.7

(a) Expected annual total seismic moment release. This calculation is based on the current gas
production plan and the current reservoir compaction model, the median seismic strain partitioning
model, and presumes there is no delay between induced strain and induced earthquakes. (b) As (a),
except for the upper bound of the 95% confidence interval of the seismic strain partitioning model.

PGA hazard assessment

The seismological model developed recently to support Probabilistic Seismic Hazard Assessments (PSHA)
for the Groningen Field (NAM, 2013) depends on future reservoir compaction. Models for reservoir
compaction are well-constrained by surface subsidence measurements. However, uncertainties in these
measurements and in the geomechanical relationship between subsidence and compaction means a range
of different compaction models are consistent with these data. These models all exhibit similar compaction
histories but predict different future compactions with differences that tend to increase with time.
Initial seismic hazard assessments were based on the linear-elastic compaction model which depends on a
single parameter for reservoir compressibility as a function of porosity. An alternative time-decay
compaction model has two parameters, the same reservoir compressibility parameter and a second
parameter for the characteristic time-scale of exponentially-distributed delays between pressure depletion
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and reservoir compaction. Monte Carlo inversion of the Groningen subsidence data indicates the
characteristic time-scale is 5±5 years. This model yields a slightly better fit to these subsidence data, but this
was achieved by adjusting two parameters not one, so future compactions predicted by this model may not
be more reliable than those from the linear model. Nonetheless, the time-decay compaction model does
offer a credible alternative compaction forecast.
The PSHA results for 2013 to 2023 based on the current production plan and the time-decay compaction
model indicate a 2%, 10% and 50% chance of peak ground acceleration, PGA, exceeding 0.67g, 0.33g
and 0.06g respectively (Figure 8.6), where g is the acceleration due to gravity taken to be 9.81 m/s 2. This
10-year period of assessment and the 2%, 10%, and 50% chances of exceedance are choices and the
hazard results obtained depend on these choices. The choice of a 2% chance over 10 years is not
equivalent to the Eurocode 8 (CEN, 2004) choice of 10% over 50 years for two reasons. First, Eurocode 8
applies to natural seismicity that does not change with time whereas Groningen experiences induced
seismicity that changes with time. This means the Groningen Field will experience a different seismic
hazard in each of the five 10-year intervals over the next 50 years. Second, Eurocode 8 is based on
seismicity composed of independent earthquakes. This means there is a particular equivalence between
probabilities of exceedance and times of exposure as described by a Poisson process. This allows the
Eurocode 8 seismic performance requirement for ground motions with a 10% chance of exceedance over
50 years (often described as a 475-year return period) to be seen as equivalent to a 2% chance of
exceedance over 10 years. This is not the case for the seismological model of the Groningen Field where
earthquakes collectively accommodate a fraction of the strain induced by reservoir compaction. This creates
a degree of inter-dependence between events so they do not exactly follow a Poisson process.
Whilst accepting there is no formal equivalence between 10% over 50 years and 2% over10 years, the
results shown in Figure 8.6a are still valid on their own although there is no equivalence with Eurocode 8.
Figure 8.7 shows results for a 5-year assessment period from 2013 to 2018 with a 2%, 10%, and 50%
chance of PGA exceeding 0.42g, 0.18g, and 0.03g respectively. These PGA values are lower because
there is less exposure time, less exposure to the statistical trend of increasing annual seismic moments, and
less uncertainty in a shorter-term forecast. Similarly for a three year assessment period from 2013 to 2016,
there is a 2%, 10% and 50% chance of PGA exceeding 0.30g, 0.12g and 0.02g respectively (Figure 8.8).
Figure 8.9 compares the probabilities of PGA exceedance for the 10-, 5- and 3-year assessments at the
location of maximum PGA.
PSHA results previously obtained for the linear-elastic compaction model (NAM, 2013) indicate a 2%, 10%
and 50% from 2013 to 2023 of PGA exceeding 0.56g, 0.27g, and 0.05g respectively. This is an example
of epistemic uncertainties due to current lack of knowledge about the mechanisms governing future
reservoir compaction on the field length-scale and the production time-scale. The following section
describes a systematic assessment of the key epistemic uncertainties and their influence on 2013 to 2023
PSHA for the Groningen Field.

(a)
Figure 8.6

(b)

(c)

PGA hazard maps for the 10 years from 2013 to 2023 with a (a) 2%, (b) 10% and (c) 50% chance of
exceedance. This assessment is based on the current production plan, the time-decay compaction model
and the Akkar et al. (2013) ground motion prediction equation modified to match accelerometer data
from the Groningen Field (NAM, 2013). The maximum PGA in each case is (a) 0.67g, (b) 0.33g, and
(c) 0.06g. The horizontal and vertical scales are Rijksdriehoek (RD) coordinates in km.

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(a)

(b)
Figure 8.7

(a)
Figure 8.8

(c)

(d)

PGA hazard maps for the 5 years from 2013 to 2018 with a (a) 1%, (b) 2%, (c) 10% and (d) 50%
chance of exceedance. The maximum PGA in each case is (a) 0.54g, (b) 0.42g, (c) 0.18g, and (d)
0.03g. The horizontal and vertical scales are Rijksdriehoek (RD) coordinates in km.

(b)

(c)

PGA hazard maps for the 3 years from 2013 to 2016 with a (a) 2%, (b) 10% and (c) 50% chance of
exceedance. The maximum PGA in each case is (a) 0.30g, (b) 0.12g, and (c) 0.02g. The horizontal
and vertical scales are Rijksdriehoek (RD) coordinates in km.

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Figure 8.9

8.8

The probability of exceedance for the maximum peak ground acceleration within the Groningen Field
for three different assessment intervals: 3, 5, and 10 years. These assessments are based on the timedecay reservoir compaction model for the current production plan and the Akkar et al. (2013) GMPE
modified to match accelerometer data from the Groningen Field.

Logic-tree to assess epistemic uncertainties

True statistical variability, known as aleatory uncertainty, represents the stochastic nature of a process and
is addressed in the approach to hazard assessment described here by random sampling of the appropriate
distributions in the Monte Carlo simulation process. Epistemic uncertainty, on the other hand, characterises
uncertainties in the values of parameters which are fixed but either unknown or known only to within a
certain precision. A logic-tree approach can be used to explore the impact of key epistemic uncertainties
once these have been identified and their expected ranges estimated. Each branch of the logic-tree
represents a distinct scenario of a particular model and associated parameter values chosen from the
spectrum of values considered to be likely. Here we describe a logic-tree approach to the characterisation
of epistemic uncertainties in the PGA hazard assessment.
8.8.1

Classification of epistemic uncertainties

The experience of running the Monte Carlo hazard simulations in the current project and expert judgement,
informed by previous studies, were used to identify the key epistemic uncertainties. Ranges of likely
parameter values were estimated based on the recent seismological and hazard studies for the Groningen
Field (Bourne & Oates 2012; Bourne & Oates 2013; Bourne et al. 2013). The logic-tree was constructed by
consideration of the main components of the seismic hazard workflow: modelling of the earthquake
catalogue to give moment release rate, event location and magnitude distributions and ground motion
predictions for these simulated earthquakes.
Each of the uncertainties identified was characterised as likely to have appreciable or negligible impact on
the hazard analysis. The uncertainties labelled as appreciable are explicitly included in the logic-tree by
having branches expressing variations above and below the trunk. For those labelled as negligible no
variation of the parameter value from the base case is considered. A third category was reserved for
uncertainties which were excluded as being beyond the scope of the current work.
The key epistemic uncertainties – that is those labelled as appreciable – for the hazard analysis are as
follows: for the source model, reservoir compaction, strain partitioning and the Gutenberg-Richter b-value;
for the surface motion estimation, the parameterisation of the ground motion prediction equation (GMPE).
These are summarised in Table 8.1. Only those sources of epistemic uncertainty classified as appreciable
have been included in the logic tree. The following paragraphs give justification for the classifications
chosen for the sources of epistemic uncertainty and the uncertainties included into the logic tree.
Reservoir Compaction
The reservoir compaction and the coupling constant which determines the amount of reservoir volume
change expressed as seismicity, are the fundamental elements in the source model. Future compaction is
predicted using a range of possible models, constrained by subsidence and compaction measurements.
These measurements are, however, not sufficient to unambiguously indicate which of the compaction
models should be used and so several models which lead to significantly different predictions for future
compaction are carried.
Strain Partitioning Factor
Similarly, there is great uncertainty in the future evolution of the value of the factor which determines the
proportion of strain expressed as earthquakes. The current value is in the range 10 -4 – 10-3 but future
escalation to values close to 1 would have a profound effect on the hazard and cannot be excluded at this
stage.
Gutenberg-Richter b-value
The Gutenberg-Richter b-value determines the relative proportion of low- and high-magnitude earthquakes
in the catalogue. It is reasonably well constrained for the catalogue as a whole to-date but there are clear
indications of significant variations of the b-value with compaction and event location. Such variations
would be expected to impact the calculated hazard and so the b-value uncertainty is also given the label
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“appreciable” and varied on branches of the logic-tree. Other characteristics of the earthquake catalogue
to date were considered not to merit separate branches of the logic-tree. Events are currently assumed to be
in the reservoir as the sparse monitoring array currently in place does not adequately constrain event
depth. Similarly earthquakes are assumed to occur by normal faulting with ruptures which do not extend
appreciably above the reservoir. It is felt that these assumptions are fairly robust but will be reassessed
when data from the upgraded Groningen monitoring array become available in 2014 and beyond.
Release of stress from tectonic sources is recognised as a possibility but is excluded at this stage given the
evidence for no historic seismicity in the north of the Netherlands.
GMPE
Seismic hazard is dependent not only on the characteristics of the earthquakes themselves but also on the
propagation of the seismic waves from the source to the surface. Empirical ground motion prediction
equations (GMPEs) are generally used to estimate PGV and PGA at a given surface location for a source of
known magnitude and location. Because of the limited history of recorded seismicity over the Groningen
field there is very little ground motion data available to constrain a Groningen-specific GMPE. This is
particularly true for events of larger magnitude and so the approach used has been to adapt GMPEs from
other regions by calibrating with local ground motion data where possible. The form of the GMPE is
therefore considered an “appreciable” epistemic uncertainty. Scale factors relative to the base-case GMPE
(the modified form of the Akkar et al 2013 GMPE) are used to represent epistemic uncertainty in the
GMPE. There is also uncertainty about the extent to which the predicted ground motion varies both from
location to location for a single event and from event to event, but this uncertainty was considered to be of
secondary importance and was labelled as “negligible”.
The set of uncertainties listed in Table 8.1 is felt to be exhaustive in that it covers all components of the
hazard analysis process and considers all parameters which were identified as having appreciable
associated epistemic uncertainty in previous studies (Bourne & Oates 2012; Bourne & Oates 2013; Bourne
et al. 2013). Other uncertainties identified were expected to be of too limited impact to justify being
explored in the logic-tree.

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Table 8.1

8.8.2

Classification of epistemic uncertainties. Uncertainties labelled as Appreciable are explicitly included in
the logic-tree. Uncertainties labelled as Negligible are considered to be well constrained or to cause
relatively small uncertainty in the hazard. These are included in the logic-tree but without any variation.
Uncertainties labelled as Excluded are not considered at this stage of the assessment. M denotes
earthquake magnitude,  denotes the total stochastic variability,  denotes the inter-event variability and
 denotes the intra-event variability.

Topology of the logic-tree

Each branch of the logic-tree shown in the figure below represents a scenario defined by a particular set of
parameter values. The main branch or trunk of the logic-tree is the base case scenario. Each branch from
the trunk represents an alternative scenario with respect to the base case. For each branch, the hazard
simulation was run with the specified parameter values and the estimated hazard compared with the base
case (scenario 0). Figure 8.10 shows the logic-tree representing the set of chosen scenarios for the hazard
calculation, following the classification of epistemic uncertainties given above. Notice that each scenario
only varies one parameter relative to the base case; covariance between parameters is not considered at
this stage. The base case scenario was taken as the linear compaction model, the P50 strain partitioning, a
b-value of 1, and the modified Akkar et al. (2013) GMPE. Note that the time-decay model has
subsequently been adopted as the preferred compaction model.

Figure 8.10

Logic-tree to represent epistemic uncertainty in the seismic hazard assessments for the Groningen Field.
a
Based on an assessment period from 2013 to 2023. bAdjusted to fit magnitude 3 to 3.6 local data by
(Bommer, 2013). Numbers inside black circles denote individual scenarios within the logic-tree. Each
scenario takes the base case scenario values (scenario 0) for all parameters with the exception of the
parameter indicated by each branch of the logic-tree.

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8.8.3

Elements of the logic-tree

Reservoir compaction
Four different compaction models are considered: the linear, bi-linear, time-decay and isotach models. The
behaviour of the four compaction models is compared in Figure 8.11, which shows the cumulative and
annual rates of bulk reservoir volume change and the cumulative and annual rates of the median seismic
moment from 1960 to 2080. Notice that the forecasts of these models agree reasonably well for the
coming 10-year period but thereafter diverge significantly from each other. In our logic-tree the linear
compaction model was taken as the base case with the time-decay and isotach models as the branches.
The corresponding strain partitioning, cumulative probability distributions of total seismic moment and
event density maps for the interval 2013 to 2023 are given in Figure 8.12, Figure 8.13 and Figure 8.14
respectively.

(a)

(b)

(c)

(d)

Figure 8.11

Comparison of the four different compaction models according to (a, b) the cumulative and annual rates
of bulk reservoir volume change and (c, d) the cumulative and annual rates of the median seismic
moment from 1960 to 2080. Grey shading denotes the 10-year hazard assessment period from 2013
to 2023.

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(a)

(b)

(c)

(d)

Figure 8.12

Results of fitting the exponential strain partitioning model to the historic earthquakes for each of the
compaction models: (a) linear, (b) bi-linear, (c) time-decay, and (d) Isotach and the current gas
production plan (BP12).

Figure 8.13

Cumulative probability distributions of total seismic moment over the next 10 years (2013-2023) for
each of the compaction models.

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Figure 8.14

(a)

(b)

(c)

(d)

Event density maps for the interval 2013 to 2023 based on four different models of reservoir
compaction: (a) linear, (b) bi-linear, (c) time-decay, and (d) Isotach. The horizontal and vertical scales
are Rijksdriehoek (RD) coordinates in km.

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Strain partitioning
Uncertainty in strain partitioning is significant. Moreover, there is uncertainty about this uncertainty in the
sense that the relative contribution of epistemic and aleatory uncertainties is currently unknown. Within this
logic-tree, we will treat the uncertainty as if it is entirely epistemic. However, in all the previous PSHA
calculations it was treated as being purely aleatory by sampling the full distribution. Consequently, logictree hazard simulations do not sample the complete distribution of total seismic moment, but instead use a
fixed value corresponding to the median or plus or minus one standard deviation from the median, that is
P50, P16 and P84 (see Figure 8.15). The P50 is the base case and P16 and P84 are the low and high case
scenarios respectively.

Figure 8.15

Epistemic uncertainty about strain partitioning is represented using the P 16, P50, and P84 quantiles of the
cumulative total seismic moment distribution.

Variation in b-value with compaction
The Gutenburg-Richter b-value characterizing the Groningen earthquake catalogue might be expected to
have a strong influence on the calculated hazard since the b-value, being the slope of the frequencymagnitude distribution, expresses the relative abundances of small and large events in an earthquake
catalogue. Figure 8.16 shows a significant variation of the b-value with compaction when the Groningen
catalogue is divided into sub-catalogues defined by compaction bins. The logic-tree base case scenario
was taken as a constant value of b=1. The alternative scenarios are defined by two compaction-dependent
hyperbolic tangent functions, with 0.5  b  1.5 and 0.8  b  1.5 respectively, and both fit the b-value
data (Figure 8.16). The 0.8  b  1.5 scenario fits the upper envelope of the data. The other scenario fits
the lower envelope of the data and has been extended to cover what is usually accepted to be the full
range of physically permissible b-values.

Figure 8.16

Three different models for the variation in b-value with reservoir compaction. Two of these models are
parameterised as hyperbolic tangent functions to represent two possible fits to the observed b-values.
The other model represents the possibility that b=1 and this apparent variation with compaction is a bias
due to the small samples of earthquakes within each compaction interval. This figure is modified from
Bourne & Oates (2013).

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Ground motion prediction equation
The choice of the ground motion prediction equation (GMPE) has a major impact on the calculated hazard.
Most of the simulations described in Bourne et al. (2013) were run using the GMPE from Dost et al. (2004);
this choice was made because it was the only available GMPE calibrated to accelerometer data from the
Netherlands (but not Groningen). Subsequent analysis of accelerometer data for Groningen events,
including the 2012 Huizinge event, showed however that Dost et al (2004) and Akkar et al (2013)
significantly overestimate surface ground motions. This motivated a modification of the Akkar et al (2013)
GMPE to match the Groningen accelerometer data whilst retaining its match to larger magnitude events
from elsewhere in Europe and the Middle East (NAM, 2013). This modified version of the Akkar et al
(2013) GMPE is now the base case GMPE for the Groningen Field and this logic-tree. The high- and lowcase scenarios were defined by scaling the PGA values by factors of 1.50 and 0.65 respectively (Figure
8.17).

Figure 8.17

Representation of epistemic uncertainty in the GMPE for the median ground motion at a hypo-central
distance of 10 km (solid line). The upper and lower alternatives to this median ground motion (dashed
lines) were scaled by 1.5 and 0.65 respectively.

These scaling factors were inferred from ranges of amplification factors applied to GMPEs used in PSHA in
western North America, which are based on the degree of constraint provided by the data from which they
are derived – see Petersen et al., (2008) and Bommer (2013). The factors adopted take into account the
fact that the Akkar et al. (2013) model is very well constrained by its database in the range of moderate
magnitudes (Mw 4-5.5), which could suggest the use of smaller scaling factors than applied to the
Californian equations used in the projects in western North America. However, consideration is also given
to the fact that in those applications the scaling factors are applied to logic trees with multiple GMPE
branches whereas for the Groningen PSHA, only a single GMPE is used. The factors are adopted for
application to the unmodified Akkar et al. (2013) equations at larger magnitudes, but conservatively
applied also to smaller events where the Groningen field data provide greater constraint on the modified
model.

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8.8.4

Results of the logic-tree

The results of the logic-tree analysis are summarised in the tornado plot, Figure 8.18. This shows the
variations in the maximum PGA found within the Groningen Field area with a 2% chance of exceedance
over the period 2013 to 2023. Notice that the strain partitioning uncertainty has the greatest impact of all
those considered – going between the P16 and P84 seismic moment budgets for the period moves the 2%
probability of exceedance maximum PGA from approximately 0.05g to about 0.6g. The GMPE scaling
and compaction model choice uncertainties have significant but much smaller impacts, moving the
maximum PGA by about 0.1g from its base case value. The b-value variations considered have negligible
impact on the calculated hazard: it seems that the competing influences of increasing event size but
reduced event number (for the large events) corresponding to reductions in the b-value approximately
balance each other out, at least in this case.

Figure 8.18

Variation in the maximum PGA found within the Groningen Field with a 2% chance of exceedance over
the period 2013 to 2023 due to epistemic uncertainties represented in the logic-tree.

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8.9

Seismic hazard assessments for different pressure depletion scenarios

8.9.1

Seismological assessments for each scenario

Pressure depletion scenarios comprise 5 elements: a geological realisation, a compaction model, a
development scenario, a production curtailment scenario, and a production philosophy (Table 8.2). From
the 288 possible permutations of these elements, a representative subset of 19 scenarios was selected for
PSHA (Table 8.3). Each of these scenarios is represented by a dynamic reservoir simulation model that was
adjusted to match historic gas production and reservoir pressure data and forecasts future pressure
depletions and a compaction model that was adjusted to match historic subsidence data and forecasts
future compactions. Based on this compaction model, the seismological model was adjusted to match the
historical earthquake statistics and forecast future seismicity. Adjustment of the seismological model involves
a Monte Carlo procedure to fit a strain partitioning model to match, within their uncertainties, the observed
variation in seismic moment with reservoir compaction. This yields an estimate for the probability
distribution of the total seismic moment released during any given time interval.
Table 8.2

Summary of the scenario elements considered for PSHA and the labels used to identify them. A complete
scenario is defined by the choice of one option under each of the different headings.

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Table 8.3

The individual scenarios selected for PSHA. The Base Case is annotated as 1.

Figure 8.19 shows the results of this analysis for all scenarios involving the time-decay compaction model
(scenarios 1 to 9). The cumulative bulk reservoir volume changes from 1960 until 2080 (Figure 8.19a) and
their annual rates of change (Figure 8.19b) are the same in all scenarios from 1960 to 2013 due to
matching the same historic data. After 2013, these start to differ in response to the different development
and production scenarios. However, on the scale of these plots most of these differences are apparently
minor with two exceptions.




The emergency stop scenario stabilises reservoir volume decrease above 350x10 6 m3, although volume
reductions continue until 2040 as reservoir pressures equalise. Thereafter, the aquifer responds and a
very small amount of the volume reduction is very slowly reversed.
The KHM1 development under a production curtailment of 30 bcm/year and the alternative production
philosophy (scenario 3) exhibits notably lower rates of reservoir volume reduction over the period
2017 to 2030; thereafter the rates are indistinguishable from the remaining scenarios.

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(a)

(b)

(c)

(d)

(e)

(f)

Figure 8.19

Comparison of the different production scenarios in combination with the time-decay compaction model
according to (a, b) the cumulative and annual rates of bulk reservoir volume change and (c, d) the
cumulative and annual rates of the median seismic moment from 1960 to 2080. Grey shading denotes
the 10-year hazard assessment period from 2013 to 2023. (e, f) Show the same curves as (c, d) except
from 2013 to 2023 and on a linear scale.

The cumulative and annual rates of the median seismic moment (Figure 8.19c-f) show similar variability
with the different scenarios. The cumulative median seismic moment in 2080 is indistinguishable for all
scenarios other than the emergency stop scenario. Because the seismological model was developed for
reservoir compaction, the results from the emergency stop scenario are applicable until 2035 but not
applicable to the subsequent slight reversal of compaction indicated after 2035. The emergency stop
scenario still shows an increasing annual median seismic moment until about 2017 (Figure 8.19f). This is
consistent with the current transit time for a pressure transient from the southern clusters of production wells
to the centre of reservoir compaction of about 5 years. During this period the median annual seismic
moment from the model increases to about 30% above the 2013 value, and does not reduce below the
2013 value until about 2021. For all other scenarios, the median annual seismic moment continues to
increase until about 2035 or later. In 2023, increases above the 2013 value vary from 2.5 (scenario 3) to
4.5 (scenario 9) times larger.
There is, of course, a critical difference between the median seismic moment and the actual seismic moment
which is modelled in PSHA for the Groningen Field as a random sample from the probability distribution of
total seismic moment. This is a Pareto sum distribution which has strongly asymmetric confidence intervals
skewed above the median. For example, there is a 10% chance of sampling a moment 10 times larger than
the median. This means there is a 10% chance of the actual seismicity exceeding 10 times the median
value. This stochastic variability is significantly larger than the variability between the different scenarios
under consideration.

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Figure 8.20 shows results for all the same scenarios, except for the isotach compaction model (scenarios 10
to 18). This isotach model is significantly different to the linear, bi-linear and time-decay models, due to the
very significant effective time-delay between changes in depletion rate and corresponding changes in
compaction rate. This means that even in the emergency stop scenario compaction continues with little sign
of ever stopping (Figure 8.20a, b). This raises significant concerns about the physical plausibility of this
particular isotach model.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 8.20

As Figure 8.19, except for the isotach compaction model. Note that the reservoir simulation for the C30
STD scenario unexpectedly terminated in 2070, so compaction artificially constant from 2070 to 2080.

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8.9.2

Ground motion assessments for each scenario

Combining the seismological model for each scenario with a ground motion prediction equation (GMPE) in
a Monte Carlo simulation yields estimates for the map distribution of peak ground velocity and acceleration
with a certain chance of exceedance. These scenario assessments were based on the GMPE developed by
Akkar et al. (2013) for Europe and the Middle East and its subsequent modification to match accelerometer
data from the Groningen Field (NAM, 2013). At present this assessment is based on simulations of 104
earthquake catalogues for the period 2013 to 2023. Results are summarised as the maximum PGA and
PGV values with a 50%, 10%, and 2% chance of exceedance (Table 8.4). For reference, maps of the PGA
with a 2% chance of exceedance are shown for all scenarios with the time-decay compaction model (Figure
8.21) and the isotach compaction model (Figure 8.22).
Table 8.4

Variability in the maximum PGA and PGV with different reservoir compaction models and production
scenarios. P50, P10, P2 denote 50%, 10% and 2% chances of exceedance respectively over the 10-year
interval from 2013 to 2023. These results are listed in descending order of the P2 maximum PGA for
each compaction model. These are preliminary results – still subject to technical assurance and liable to
change.

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The estimated maximum PGA with a 2% chance of exceedance is between 0.69g and 0.64g for all
scenarios involving the time-decay compaction model with just two exceptions: 0.41g for the emergency
stop scenario and 0.58g for the KHM1 C30 Alternative (Tremor) scenario. Relative to the 0.66g for the
base case SN STD scenario, these two exceptions represent apparent hazard reductions of 38% and 12%
respectively over the period 2013 to 2023.

Figure 8.21

Influence of the different depletion scenarios on the PGA hazard. PGA hazard maps are shown for a
2% chance of exceedance over the period 2013 to 2023 based on the time-decay compaction model.
The horizontal and vertical scales are Rijksdriehoek (RD) coordinates in km.

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Figure 8.22

8.10

As Figure 8.21, except for the isotach compaction model. The horizontal and vertical scales are
Rijksdriehoek (RD) coordinates in km.

Hazard Summary

Probabilistic seismic hazard assessment for the 10 years from 2013 to 2023 based on the time-decay
reservoir compaction model indicates a 2% chance of the peak ground acceleration (PGA) exceeding
0.67g, where g is the acceleration due to gravity taken to be 9.81 m/s2. These results were obtained using
a Monte Carlo method (NAM, 2013) and the Akkar et al (2013) ground motion prediction equation
modified to be consistent with ground motion data from the Groningen Field, and by including all
earthquakes of at least magnitude1.5. This hazard should not be considered equivalent to the 10% chance
over 50 years stipulated by Eurocode 8 because induced seismicity within the Groningen Field varies with
time and is not modelled as a Poisson process. Repeating this assessment method for the 5 years from
2013 to 2018 indicates a 2% chance of PGA exceeding 0.42g. For the three years from 2013 to 2016,
the same assessment method yields a 2% chance of exceeding 0.30g.
Significant epistemic uncertainties exist in the seismic hazard assessment for the Groningen Field; these are
primarily associated with strain partitioning, the GMPE and reservoir compaction. This motivates further
data acquisition and studies designed to reduce these uncertainties. The epistemic uncertainty attributed to
strain partitioning will in some part be due to random variability (aleatory). The relative contribution of
epistemic and aleatory uncertainties to the overall uncertainty in strain partitioning is unknown. Further
earthquake, surface acceleration, and subsidence monitoring within the Groningen Field will provide more
information that may help to better constrain these uncertainties.
Probabilistic seismic hazard assessments from 2013 to 2023 were conducted for 19 distinct scenarios that
form a representative sample of the different geological, compaction, development, and production
scenario elements. The results obtained indicate all these depletion scenarios yield similar seismic hazard
estimates (within 5%) with two exceptions. The emergency stop scenario reduces the maximum PGA with a
2% chance of exceedance to 0.41g. This represents a 38% reduction compared to 0.66g for the current
case (SN STD). Alternatively the “30 Bcm/year curtailment combined with the alternative production
philosophy (KHM1 C30 Tremor) scenario reduces the hazard to 0.58g, 12% below the current case.
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In the emergency stop scenario, the isotach model predicts significant rates of compaction will persist until
long after 2080. There is no analogue field evidence to support such unexpected behaviour. Equally,
laboratory measurements are unable to constrain the isotach model behaviour on the multi-decadal timescale exhibited here. For these reasons, it is desirable to conduct an independent validation of the
applicability of the isotach model to the Groningen case.

8.11

Conclusions

The main findings with respect to seismic hazard are summarized below:



A Monte-Carlo approach to Probabilistic Seismic Hazard Assessment (PSHA) was identified as being
best suited to the analysis of the Groningen field’s induced seismicity.



Independent PSHA implementations in Python and C gave the opportunity for valuable cross checking
of hazard estimates.



Summary of the maximum magnitude and maximum PGA values with 50%, 10% and 2% chances of
exceedance over three different time periods of assessment according to the current production plan,
the time-decay compaction model and the modified Akkar et al. (2013) GMPE is given below:
Period











Maximum Magnitude

Maximum PGA

P50

P10

P2

P50

P10

P2

2013 - 2016

3.4

4.1

4.6

0.02g

0.12g

0.30g

2013 - 2018

3.6

4.4

4.9

0.03g

0.18g

0.42g

2013 - 2023

3.9

4.8

5.3

0.06g

0.33g

0.67g

Eurocode 8 has been adopted as a standard framework for the assessment of the seismic capacity of
buildings in the Groningen field area. Consideration of PGA and PGV values corresponding to the
475-year return period specified in Eurocode 8 requires that a specific production period is chosen for
calculating the annualized hazard due to the planned production and expected compaction.
Direct comparisons with published 475-year hazard maps can be very problematic because such
standard hazard maps generally impose a lower minimum magnitude limit than the M=1.5 used here,
such as M=5 in California and M=4.5 in Europe.
Disaggregation of the hazard showed that the seismic hazard is dominated by events of intermediate
magnitudes (M = 4 – 5). This contrasts with the maximum possible magnitude, M6.5, implied by the
compaction-based seismicity model, showing that the induced seismic hazard is not well characterized
by Mmax.
The most important uncertainty affecting the calculated hazard is the very large uncertainty in the
partioning coefficient relating seismic moment budget to the volumetric strain due to compaction.
Sensitivity tests showed that the selection of a suitable ground motion prediction equation (GMPE) and
the future production period chosen for calculating the annualized hazard had a strong influence on
the resulting hazard estimates. Changes to the b-value however had limited impact on the calculated
results.
Probabilistic seismic hazard assessment for the 10 years from 2013 to 2023 based on the time-decay
reservoir compaction model indicates a 2%, 10% and 50% chance of peak ground acceleration, PGA,
exceeding 0.67g, 0.33g and 0.06g respectively, where g is the acceleration due to gravity taken to be
9.81 m/s2.



For a 5-year assessment period from 2013 to 2018 the corresponding results are a 2%, 10%, and
50% chance of PGA exceeding 0.42g, 0.18g, and 0.03g respectively. Similarly for a three year
assessment period from 2013 to 2016, there is a 2%, 10% and 50% chance of PGA exceeding 0.30g,
0.12g and 0.02g respectively.



PSHA results previously obtained for the linear-elastic compaction model (NAM, 2013) indicate a 2%,
10% and 50% chance from 2013 to 2023 of PGA exceeding 0.56g, 0.27g, and 0.047g respectively.

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

These PSHA results were obtained using a Monte Carlo method (NAM, 2013) and the Akkar et al
(2013) ground motion prediction equation modified to be consistent with ground motion data from the
Groningen Field, and by including all earthquakes of at least magnitude 1.5.



It is important to point out that the 2% over 10 years hazard results listed above should not be
considered equivalent to the 10% chance over 50 years stipulated by Eurocode 8 because induced
seismicity within the Groningen Field varies with time and is not modelled as a Poisson process.







Epistemic uncertainty characterises uncertainty in the values of parameters which are fixed but either
unknown or known only to within a certain precision. Significant epistemic uncertainties exist in the
seismic hazard assessment for the Groningen Field; these are primarily associated with strain
partitioning, the GMPE and reservoir compaction. A logic-tree approach, in which each branch of the
logic-tree represents a distinct scenario of a particular model and associated parameter values, was
used to explore the impact of the key epistemic uncertainties on the estimated hazard. It was found that
the strain partitioning uncertainty has the greatest impact of those considered. The epistemic
uncertainty attributed to strain partitioning will in some part be due to random (aleatory) variability,
representing the stochastic nature of the earthquake process. The relative contribution of epistemic and
aleatory uncertainties to the overall uncertainty in strain partitioning is unknown. Further earthquake,
surface acceleration, and subsidence monitoring within the Groningen Field will provide more
information that may help to better constrain these uncertainties.
Probabilistic seismic hazard assessments from 2013 to 2023 were conducted for 19 distinct scenarios
that form a representative sample of the different geological, compaction, development, and
production scenario elements. The results obtained indicate all these depletion scenarios yield similar
seismic hazard estimates (within 5%) with two exceptions. The emergency stop scenario reduces the
maximum PGA with a 2% chance of exceedance to 0.41g. This represents a 38% reduction compared
to 0.66g for the current case (SN STD). Alternatively the KHM1 C30 Alternative (Tremor) scenario
reduces the hazard to 0.58g, 12% below the current case.
In the emergency stop scenario, the isotach model predicts significant rates of compaction will persist
until long after 2080. There is no analogue field evidence to support such unexpected behaviour.
Equally, laboratory measurements are unable to constrain the isotach model behaviour on the multidecadal time-scale exhibited here. For these reasons, the reliability of this particular isotach model is
entirely doubtful.

8.12

Recommendations for further work

The following additional work, and subsequent updating of the seismic hazard assessment, should be
considered to address some of the key uncertainties discussed above.







Identify and investigate new avenues which have the potential to better constrain the partitioning
coefficient relating seismic moment budget to the volumetric strain due to compaction.
Investigate and characterize any evidence for time delays between reservoir compaction and induced
seismicity.
Reduce uncertainty about the depth distribution of events using deep geophone arrays in wells near
Zeerijp and Stedum.
Investigate the spatial distribution and correlation length-scale of site specific (intra-event) uncertainty
within the ground motion prediction equation. If necessary, include this effect in the seismic risk
assessment.
Further reduce uncertainty in the ground motion prediction equation by acquiring more surface strong
motion measurements within the Groningen Field.

8.13

References

Akkar, S., Sandıkkaya, M.A. and Bommer, J.J. Empirical ground-motion models for point- and extendedsource crustal earthquake scenarios in Europe and the Middle East. Bulletin of Earthquake Engineering
(2013), in press, DOI 10.1007/s10518-013-9461-4.

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ATC (1976). Tentative provisions for the development of seismic regulations for buildings. Report ATC 306, Applied Technology Council, Redwood City, California, USA.
Bazzurro, P. and Cornell, C.A. Disaggregation of Seismic Hazard. Bulletin of the Seismological Society of
America (1999), Vol. 89, No. 2, pp. 501-520.
Bommer, J. (2013). Proposals for New GMPEs for the Prediction of PGA and PGV in the Groningen Gas
Field (pp. 1–37). London.
Bommer, J.J. & R. Pinho (2006). Adapting earthquake actions in Eurocode 8 for performance-based
seismic design. Earthquake Engineering & Structural Dynamics 35(1), 39-55.
Bommer, J.J. and Abrahamson, N.A. Why Do Modern Probabilistic Seismic-Hazard Analyses Often Lead
to Increased Hazard Estimates? Bulletin of the Seismological Society of America (2006), Vol. 96, No. 6,
pp. 1967–1977.
Bommer, J.J. and Crowley H. The Influence of Ground-Motion Variability in Earthquake Loss Modeling.
Bulletin of Earthquake Engineering (2006), Vol. 4, pp. 231–248.
Bommer, J.J. and Stafford, P.J. Seismic hazard and earthquake actions in Seismic Design of Buildings to
Eurocode 8, edited by Elghazouli, A.Y. Spon Press (2009) pp. 6-46.
Bommer, J. (2013). Epistemic Uncertainty in Groningen Field GMPEs for PGA (pp 1–8). London.
Bourne, S J, & Oates, S. (2012). Probability of an earthquake greater than magnitude 4 due to gas
production from the Groningen Field (pp. 1–40). Rijswijk, The Netherlands.
Bourne, S., Oates, S., and Bommer, J.J. A probabilistic seismic hazard assessment for the Groningen Field.
Technical Report. Shell Global Solutions International. Rijswijk, The Netherlands (2013).
Bourne, S.J., & Oates, S. (2013). Induced strain and induced earthquakes within the Groningen Gas Field:
Earthquake probability estimates associated with future gas production (p. 67). Rijswijk, The Netherlands.
CEN (2004). Eurocode 8: Design of Structures for Earthquake Resistance. Comité Européen de
Normalisation, Brussels, Belgium.
Crowley H. and Bommer, J.J. Modeling Seismic Hazard in Earthquake Loss Models with Spatially
Distributed Exposure. Bulletin of Earthquake Engineering (2006), Vol. 4, pp. 249–273.
Dost, B., van Eck, T. and Haak, H. Scaling of peak ground acceleration and peak ground velocity recorded
in the Netherlands. Bolletino di Geofisica Teorica ed Applicata (2004), Vol. 45, No. 3, pp. 153-168.
Grant, D.N., J.J. Bommer, R. Pinho, G.M. Calvi, A. Goretti & F. Meroni (2007). A prioritization scheme for
seismic intervention in school buildings in Italy. Earthquake Spectra 23(2), 291-314.
Haak, H.W. and De Crook, T. Seismische analyse van aardbevingen in Noord-Nederland. KNMI report
WR 94-1. KNMI, de Bilt (1994).
Houtgast, G. Aardbevingen in Nederland. KNMI report 179. KNMI, de Bilt (1992).
Main, I., Irving, D., Musson, R. and Reading, A. Constraints on the frequency-magnitude relation and
maximum magnitudes in the UK from observed seismicity and glacio-isostatic recovery rates. Geophysical
Journal International (1999), Vol. 137, pp. 535-550.
NEHRP (2009). Recommended Seismic Provisions for New Buildings and Other Structures. Part I Provisions. National Earthquake Hazards Reduction Program, Federal Emergency Management Authority
(FEMA), Washington D.C., USA.
NZSEE (2006). Assessment and Improvement of the Structural Performance of Buildings in Earthquakes.
New Zealand Society for Earthquake Engineering, Wellington, New Zealand.
Petersen, M.D., A.D. Frankel, S.C. Harmsen, C.S. Mueller, K.M. Haller, R.L. Wheeler, R.L. Wesson, Y.
Zeng, O.S. Boyd, D.M. Perkins, N. Luco, E.H. Field, C.J. Wills & K.S. Rukstales (2008). Documentation for
the 2008 Update of the United States National Seismic Hazard Maps, USGS Open-File Report 20081128, US Geological Survey, Reston, VA. 61 pp.

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9 The Role of Faults
9.1

Introduction

Reservoir compaction and subsidence is observed in all producing oil and gas fields that exhibit reservoir
depletion. However, in many fields this does not lead to seismic events. Various fields in the South China
Sea exhibit seabed subsidence of more than 4 meter without any reported seismic event [Davison, 2010].
Also, most depleting and compacting fields in the Netherlands have not shown seismic events to date [van
Thienen, 2012]. The occurrence of reservoir depletion or reservoir compaction is therefore not necessarily
leading to seismic events.
Seismic events mark the release of stored energy from the subsurface [Kanamori, 1994]. Stored energy is
available in both the gas phase as well as strain energy in the solid rock structure. In general, production
operations increase the level of strain energy that is available in the subsurface. The fact that seismic events
are observed in the Groningen field is an indication that at least part of the strain energy is recoverable. In
principle, the recoverable strain energy can either be the production-induced energy (induced seismicity) or
the energy available by tectonic processes (triggered seismicity).
At least two conditions need to be satisfied to explain the occurrence of seismic events in the Groningen
field:
1.
2.

The availability of recoverable strain-energy from the subsurface and
A failure mechanism to release (a part of) it.

Fault slip is a commonly accepted failure mechanism for the release of strain energy in seismic events
[Kanamori, 1994]. However, the occurrence of fault slip cannot be considered in isolation from the
behavior of surrounding formations in the subsurface, because in particular the formations in the vicinity of
a slipping fault are the source of the released energy. It is imperative to understand how and where in the
subsurface the depletion-induced stress change is accommodated: in reversible or irreversible formation
deformation.
In this chapter the role of faults in the occurrence of seismic events is outlined in a geomechanical context.
The release of energy in seismic events is related to unloading of rock mass in the vicinity of faults. Also, the
development of irreversible deformations (compaction strain, salt creep or other time-dependent behavior)
in and around the depleting reservoir plays an important role in the potential development of critical stress
conditions on fault planes. First, the geomechanical evaluation framework is outlined to introduce the
requirements for an improved understanding of the geomechanical response during seismic events in the
Groningen field. Subsequently, the gaps in our knowledge and the status of ongoing modeling studies are
outlined.

9.2

Seismicity in a geomechanical evaluation framework

Geomechanical evaluations take into account:
A.
B.
C.
D.

equilibrium of forces (stress), either statically or dynamically,
the (subsurface) structure that ensures that equilibrium is satisfied,
the material behavior of various components of the subsurface structure
the (depletion) loading on the subsurface structure

The displacements of and the forces acting on the subsurface structure are the independent parameters
used to describe external equilibrium (Figure 9.1). The subsurface loading normally takes the form of a
changing pore pressure. The subsurface structure responds with deformation (strain) in order to mobilize
the required stress in any material point to ensure internal equilibrium. The stress-strain relationship that
describes this response is also referred to as material behavior or the constitutive model. The subsurface
structure, i.e. the layering and dipping of formations, presence of faults and formation offsets etc, is
relevant in the determination of the stress re-distribution mechanism due to the imposed loading condition.
In applied mechanics this is referred to as the kinematic relationship between displacements and strain.

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Figure 9.1

9.2.1

The external force F and internal stress condition  are in equilibrium; the constitutive model describes
the relationship between stress  and strain , which results in a displacement (elongation) u at location
where the external force F is applied. Either the force F or the displacement u can be prescribed. The red
point represents a material point that is at the onset of failure, whereas the green point is assumed in an
elastic deformation state.

Stored energy

Seismic events mark the release of stored energy from the subsurface [e.g. Kanamori, 1994]. From a
geomechanical point of view, the subsurface moves from one state of equilibrium just before a seismic
event, to another state of equilibrium with a lower level of stored energy just after the event. The stored
energy in the subsurface is described by the constitutive behavior of the subsurface formation, while the
energy difference between the equilibrium states can be released partly as seismic energy and also by
various other energy sinks, for instance thermal energy.
The work done by an external force in Figure 9.1 (Work A = F * u ) is stored in material points in the form
of strain energy. This is the area under the stress-strain relationship as indicated in Figure 9.2. The total
strain energy is calculated by considering the stress-stain relationship of each material point of the
specimen in Figure 9.1 or a “control volume” of the subsurface. The stored energy level at the beginning of
field production is determined by the “virgin” in-situ stress condition (the green point in Figure 9.2).
Additional energy is stored in material points that undergo an increased effective stress level due to field
production.

Figure 9.2

Stress-strain relationship showing a material point in an elastic state (green) and one at the onset of
failure (red). See also Figure 9.1. The strain energy stored in a material point is represented by area
under the stress-strain relationship. Additional energy is stored for any material point gaining strain (red
point), whereas energy is released by material points that are unloading (green and orange points).

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9.2.2

Failure

Any material has a limit to what stress level can be carried. This limit can be expressed as the tensile
strength in case of tensile loading (mode 1), or the shear strength in case of shear or mode 2 loading
(mode 3 failure is ignored). The stress carrying capacity of a material point can also be limited in terms of
a compressive stress, which may lead to compressive failure (pore collapse) when exceeded. These failure
mechanisms, which are shown conceptually in Figure 9.3, are all expressed in terms of a limit stress.
Numerous models have been developed to describe the onset and post-failure behavior of all kind of
materials under various loading conditions.
The post-failure behavior of a material point can be classified in so-called hardening and softening
behavior (Figure 9.2). In the case of hardening behavior the stress carrying capacity still increases after the
onset of failure, while it decreases in case of softening behavior. The strain energy level increases in both
cases.

Figure 9.3

Mohr-circle diagram relating the principal stress state to possible tensile, shear and compressive failure
conditions.

Figure 9.4

Strain energy is released upon unloading of a material point. Part of the strain energy may be
irrecoverable, which is expressed by a remaining strain after full unloading.

9.2.3

Release of stored energy

Strain energy is released by material points that follow an unloading branch as indicated in Figure 9.4. In
an idealized world, material behavior up to the onset of failure is assumed linear and elastic. This implies
that no strain remains after removal of the stress (green point). Material failure is often the onset of a
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nonlinear stress-strain relationship and is associated with the development of irrecoverable (in-elastic)
strain. The orange point in Figure 9.4 shows the unloading behavior of a material point that has been in a
post-failure state, in which not all strain energy is released.
Loading and unloading material points are both present in (subsurface) structures in which failure occurs. In
case of tensile failure in the sample in Figure 9.1, the red point will show post-failure softening behavior,
whereas the green material point enters an unloading branch as indicated in Figure 9.4. This is enforced
by equilibrium of the sample. The green points release strain energy and the red points store strain energy.
More energy is released if more material points respond like the green point. Unstable fracture
propagation may occur if the energy released by the green points is larger than the energy stored in the
red points.
9.2.4

The role of faults and the release of stored energy in the Groningen field

Seismic events are not possible without the occurrence of a failure mechanism. Fault slip is a known failure
mechanism that releases strain energy from the subsurface [Kanamori (1994), Mulders (2003)]. As
outlined in the previous sections, the released energy originates from formations that undergo stress
unloading. It is therefore important to understand the stress loading and unloading behavior of formations.
Energy dissipated by creep and other irrecoverable deformation processes in subsurface formations limits
the available energy for release during a potential seismic event. The stress carrying capacity of faults at
the onset of slip and their post-failure behavior determine what equilibrium conditions are possible and
thereby the associated stored energy levels of adjacent formations.
At present, it is assumed that the earthquakes in the Groningen field are induced by gas production in the
depleting reservoir and not by fault slip outside the depletion area. The left-hand side of Figure 9.5 shows
the principle of seismicity by fault slip away from the depleting reservoir, whereas the right-hand shows
reservoir depletion-induced seismicity, in which a growing Mohr circle touches the shear failure line. The
assumption of reservoir depletion-induced seismicity is based on observations from other fields like
Roswinkel (down-hole geophone trial, ABB, 2001) and the Bergermeer UGS (down hole geophone, pers.
com. TAQA, KNMI, TNO). Both fields show seismicity mainly at reservoir depth. Also it is observed that a
producing gas field records the first earthquake only after a certain pressure drop (van Eijs et al. 2006).
This indicates that the normal faults in the Dutch subsurface are not critically stressed initially.
Based on currently available data, it cannot be ruled out that the seismic events originate from fault slip
occurring above or below the depleting reservoir units.

Figure 9.5

Principle of seismicity occurring outside the reservoir (left, taken from Segall, 1989) and depletioninduced seismicity (right, taken from Mulders, 2003)

A large number of faults have been interpreted in the Groningen field. The location of 707 major faults
relative to the recorded seismic events is shown in Figure 9.6. This subset has been used for the construction
of static, dynamic and geomechanical models. The subset of approximately 1000 minor faults has been
disregarded for practical reasons. (Note that the extent to which these small faults, and even smaller subseismic faults, play a role in the geomechanical behavior of the reservoir rock is yet to be established). The
faults in Figure 9.6 Are color-coded based on the number of seismic events within 500 meter distance. This
implies that individual seismic events may be assigned to multiple faults. The range of 500 meter is based
on the approximate accuracy of the current seismic monitoring network. Seismicity by slip along existing
geological faults is a plausible explanation, although other failure mechanisms cannot be ruled out based
on available field data.

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Figure 9.6

9.3

Groningen fault map with epicenters of recorded seismic events > 1.5. Color of faults indicates the
number of events within 500 m distance from each fault. A selection of wells drilled in the area is
indicated by the blue dots. Faults shown are at Top_Rotliegend level

What we know and what we don’t know (yet)

In this section, an attempt is made to outline what is known and what is not known in terms of the
geomechanical evaluation framework. Virtually all modeling elements of the framework need substantial
improvement to make a step forward in the understanding of seismicity. At the same time many of these
elements are difficult to constrain by field data. In principle the same strategy is followed that has lead in
the past to successful approaches in the prevention of borehole instability and the management of sand
production. In each section possible field monitoring options are identified to test modeling assumptions
and constrain uncertainties. Subsequently, the following topics are discussed:
A.
B.
C.
D.

equilibrium of forces (stress), either statically or dynamically,
the (subsurface) structure that ensures that equilibrium is satisfied,
the material behavior of various components of the subsurface structure
the (depletion) loading on the subsurface structure

9.3.1

Equilibrium of forces (stress), either statically or dynamically

Seismic events are characterized by acceleration and deceleration of mass (material points). The
conventional approach in geomechanical modeling is to assume static equilibrium, which is appropriate to
simulate the onset of failure (such as fault slip) and even post-failure behavior as long as stable equilibrium
can be demonstrated. Possibly, also the static stress distribution after a seismic event could be assessed.
However, the internal stresses induced by acceleration require a vibration (dynamic) analysis.
Vibration analysis considers the forces associated with mass velocity and accelerations measured during
seismic events. In theory, vibration analysis can address triggering of fault slip induced by slippage of
neighboring faults if incorporated in a single dynamic system. However, industry experience in conducting
this type of evaluation is very limited. Vibration analysis requires validation of additional modeling
assumptions as well as calibrations of its parameters by dedicated field data, while modeling static
equilibrium is already challenging.

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A pilot study is scheduled for late 2013 to investigate how vibration analysis can contribute to a better
understanding of the seismic events in the Groningen field. The pilot study is conducted on a 2-dimensional
model and aims to reveal the importance of inertia effects on the stress distribution and the onset of fault
slip. The pilot study should provide directions for this line of investigation.
9.3.2

Subsurface structure

A recently updated static subsurface model is available [Visser, 2012]. The static model determines the
production-induced stress re-distribution in the subsurface in a geomechanical analysis. Formation layers
that have a similar constitutive model may be joined together for geomechanical assessment. Up-scaling is
applied to eliminate geometrical detail that is irrelevant to the risk under consideration, while maintaining
the geomechanical response at large. More than 1700 faults have been interpreted in the Groningen field,
of which 707 have been used to construct the static and dynamic reservoir model. Currently,
geomechanical evaluation of all 707 faults in a single geomechanical model cannot be conducted
realistically. Therefore, simplifications are incorporated in ongoing 3D modeling efforts, and dedicated
studies are being conducted to reduce the modeling uncertainties (see section 9.4).
Key geometrical parameters, such as fault location, dip and azimuth angle are available in 3D. Also depth
and thickness of adjacent formations and the throw across the fault can be obtained from the static
subsurface model. These geometrical aspects have been found important in the determination of the onset
of fault slip [Mulders, 2003], and are being used in detailed 2D analysis (see section 9.4.2). However, the
available level of detail cannot be satisfied to the same level in 3-dimensional evaluations.
In general, geometrical detail that is not captured by seismic acquisition is not available. This includes subseismic faults, and formation offset along known and unknown faults. Also, other asperities that may induce
local stress disturbances, and could trigger the onset of failure, are not captured.
An important factor in modeling the geomechanical response of the Groningen field is the required level of
geometrical detail. Fault orientation and other geometrical aspects (asperities) are known to be very
important in the prediction of slip onset, while the static model accuracy may be too limited due to the
seismic resolution. On the other hand, computer capacity is also too limited to include all detail required for
appropriate assessment. In summary, it is assumed that sufficient field detail is available for practically
geomechanical evaluation, but more detail may improve assessment potential fault slip areas in the future.
Seismic field monitoring needs to demonstrate the validity of the simplifications made, and whether or not
the Groningen earthquakes can be associated with the current fault set. The uncertainty in the hypocenter
locations with the current monitoring network is simply too large to draw that conclusion now. The down
hole geophones will hopefully reveal whether the present model accuracy is at its right level of detail. The
fault model can be considered to be appropriate if most earthquakes can be mapped.
9.3.3

Material behavior

The pre- and post-failure behavior of all formations is important for estimating the subsurface response to
pressure depletion resulting from production (see section 9.2). In general, the mechanical contribution to
the response reduces with the distance from the depletion area. The following models and parameters are
relevant:





Elastic response by Young’s modulus (or compressibility) and Poisson’s ratio. This is relatively well
established for the Slochteren Sandstone from numerous experiments aimed at predicting compaction
and subsidence in the field. However, these tests were done at relatively high loading rates. Lower
loading rates normally lead to more strain (see also the description of the Isotach model in section 4).
Furthermore, stiffness contrasts are less well understood due to the lack of measurements and the
complex interaction of fluid dynamics with mechanical behavior (drained versus un-drained)
Onset of failure in subsurface formations (the tensile, shear and compressive failure model and their
associated parameters). Various lab experiments have shown that the onset of (shear) failure in the
Slochteren Sandstone occurs at a high stress level (Holt, 2011), higher than expected stress paths in the
Groningen field during the full production cycle. It is realized that there uncertainties related to the
upscaling of lab test results to field-scale, and the impact of pre-existing defects within the rock mass.

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






The post-failure behavior of the Slochteren Sandstone and Ten Boer Claystone has not been
investigated.
Unloading and cyclic behavior of the Slochteren Sandstone and Ten Boer Claystone in particular.
Cyclic loading was studied in detail as part of an investigation to the Norg underground gas storage.
A total of 70 cyclic loading tests were carried out on 5 core samples. The first cyclic loading resulted in
a significant reduction of pore compressibility and compaction coefficient. This may partly be attributed
to core damage. A further slight reduction in pore compressibility was recorded over cycle 2 to 5. A
very small amount of un-recoverable strain was measured after each cycle, and attributed to creep
behavior. After the fifth cycle the samples behave near elastic over the full reservoir pressure
operational range between 33 and 10 MPa with a compaction coefficient of around 6*10 -5MPa-1.
The shear stress carrying capacity of faults and their post-failure behavior. Some models have been
proposed in which slip velocity is incorporated, such as rate and state slip models. The conversion of
strain energy into thermal energy needs further attention. However, this is a fundamental uncertainty
that depends on details of the dynamic rupture process and might be a limitation to further constitutive
modelling.
The variability of the constitutive behavior of formations and fault properties over the field. It is
assumed that the behavior varies as a function of lithology and stress history. In particular the shear
stress carrying capacity of faults is considered an important parameter. The quantification of this
variability is not possible at this stage.

The main objective of additional constitutive modeling is to improve the accuracy and distribution of
production-induced strain energy in the reservoir. The most important improvement step may arise from a
long term compaction study with a particular focus to time-dependent subsidence behavior conducted by
NAM and supervised by the “Waddenacademie (KNAW, 2013)”.
9.3.4

Loading conditions, initial and production-induced

A distinction is made between initial loading and production-induced loading. The initial loading includes
the determination of the virgin in-situ stress direction and magnitude in the field, as well as the pore
pressure. The production-induced loading comprises the change of pore pressure and in-situ stress
condition.
Initial Stress field direction and magnitude
The available stress data in the Netherlands suggest a normal faulting stress regime, with the assumption
that the maximum principal stress is parallel to the vertical or overburden stress, and the minimum and
maximum principal stress components are both in a horizontal direction. Principal stress values and their
directions are key parameters for the analysis of possible reactivation of faults at the reservoir level in the
Groningen Field. This section describes the interpretation of available data and the recently acquired data
from the Borgsweer-5 well.
The vertical total stress is considered well established, and has been derived from the integration of the
density log with depth. More than ten wells have an almost complete coverage of the density from surface
to end depth. The variation in stress gradient at the reservoir is limited and mainly caused by thickness
variations in the overlying Zechstein salt.
The minimum horizontal stress is not properly established for the Groningen field. The fracture closure
pressure that can be interpreted from a rock strength test is the closest approximation of the value of the
total minimum horizontal stress, which in our case is assumed to be the minimum horizontal stress or Sh.
Stress values from rock strength test data were reported by Bouts (2000) and Hettema et al. (2000). The
best estimate of the values including uncertainties is given in Hettema (2000). A thorough investigation
concluded that the three data points that have been used in previous reports and publication are not
reliable and should not be used in any subsequent study. Consequently, the only reliable information
available from the Groningen rock strength tests was obtained during depletion. The rejection of the initial
data points makes an assessment of an initial value of the Sh cumbersome. The extrapolated value from a
straight line through only two data points yields a value for the initial stress of 1.6 bar/10m at an initial

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pore pressure of 350 bar with a depletion constant of 0.6 (Figure 9.7). These values were used as the best
guess values in the subsequent finite element models described further on, but are highly uncertain.

Figure 9.7

Left: Historical rock strength tests from wells in the Groningen field and the trend line used to derive
values for the initial stress and depletion constant for further studies. Right, same figure with the inclusion
of the new Borgsweer-5 data point.

The new rock strength test in Borgsweer-5 adds a new data point to this plot (van der Bas, 2013) visible in
the right plot in Figure 9.9. It confirms the hypothesis that the stress and/or depletion constant is likely to be
variable in the field, but makes a proper choice of a general value for the minimum total stress in further
modeling attempts difficult. Therefore, the future acquisition of more data is an absolute necessity.
Multiple sources suggest a value for the maximum horizontal stress between 1.05 and 1.15 times the
minimum horizontal stress. In the previous section the main focus was on the derivation of the magnitude
and direction of the minimum principal stress. The value for the intermediate stress, which in our case is
equivalent to the maximum horizontal stress or SH, cannot be measured directly. It can be obtained by
finding a value for the stress ratios and then, by knowing the value of the minimum stress, calculate a value
for the intermediate stress. Stress ratios can be derived from Differential Strain Analysis (DSA), breakout
inversion and circumferential sonic logging tools like the sonic scanner that was run in the recently drilled
Borgsweer-5 well. The results from these analyses were consistently showing a weak anisotropy where the
SH is 5 to 15% higher with respect to the Sh. An average value of 10% will be used in subsequent studies.
The maximum horizontal stress direction is relatively well constrained from image logs. Image logs in a
near vertical well can reveal information on a possible horizontal anisotropic stress field (e.g. Brudy and
Kjørholt, 2001) when it is assumed that one of the principal stress directions is aligned parallel to the
vertical axis. This approach has been pursued in an earlier study by van Eijs and van Dalfsen (2004)
where stress directions have been mapped for the available data in the Netherlands. Additional data was
found in the NAM data and interpreted. Six wells in the Groningen area (Wildervank-4, Rysum-Z-1,
Rodewolt-1, Oude Pekela-2, Kiel Windeweer-1, Bierum-13) have image log data. Only two of them
(Rodewolt-1 and Kiel Windeweer-1) contain clear expressions of breakouts. Kiel Windeweer -1 has a
good quality image and shows breakouts over multiple sections (Figure 9.8).
The average direction for the SH derived from this data is 160°-340°. The Rodewolt-1 well contains poor
breakout data for only three sections of breakouts. An estimated direction for the SH in this well is 155°335°. An overview of the interpreted available data is given Figure 9.9.

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Figure 9.8

Part of the Kiel Windeer-1 image log showing a consistent pattern of breakouts

Rodewolt-1

Kiel Windeweer-1

Figure 9.9

SH directions for the Groningen area. The new data (with labels and a blue dot in the middle) have
been combined with information from the world stress map database

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Production induced stress changes
The rock strength tests illustrated in Figure 9.7 show the dependence of the Sh on the depletion. Usually an
average value for the depletion constant can be derived from this type of data, but currently the number of
data points is too limited. For subsequent geomechanical modeling it was suggested to use a best estimate
value of 0.6 or a stress-depletion relation based on the Poisson’s ratio and Biot- alpha. The latter method is
followed by Exxon-Mobil in their 3D finite element approach. The impact of the depletion on the Sv has not
been investigated but is expected to be negligible. The gas production has been history matched to the
reservoir engineering model by allowing a high transmissibility for the faults, but it cannot be excluded that
gas or water is trapped within faults. This high pressure in the faults could increase the possibility of fault
slippage. The same process may also play a role where water from the underlying aquifer preferentially
flows into highly permeable faults.

Stress-Path Coefficient
700

Measure Horizontal Stress [bar]

600

y = 0.5405x + 350.49
R² = 0.5973

500

y = 0.6395x + 251.29
R² = 0.5805

y = 0.5751x + 258.35
R² = 0.9911

400

Blija
Ameland

y = 0.8334x + 160.8
R² = 0.9836

Groningen
Coevorden

300

Linear (Blija)
Linear (Ameland)
Linear (Groningen)
200
Linear (Coevorden)

100
100

200

300

400

500

600

700

Measured Reservoir Pressure [bar]

Figure 9.10

9.3.5

The (minimum) horizontal stress measured in different fields in The Netherlands at various levels of
reservoir depletion and the derived stress-path coefficient (gradient of the linear trend lines).

Monitoring activities and further investigations

To test the hypothesis that seismicity occurs at the reservoir and at the faults as a result of poro-elastic stress
changes, NAM will install two down hole arrays in the Zeerijp and Stedum monitoring wells (Figure 9.11).
Based on modeling, these arrays will be able to detect and locate events in the micro-seismic domain M>-3
with a location accuracy of around 50m for a M=-1.5 event at an occurrence distance of around 500m.
This is a very critical step in the assessment of the concept of induced seismicity that also forms the
foundation of our current 2D and 3D finite element (FE) models.

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Figure 9.11

3D view towards the north of the top reservoir structure map with the subsurface position of the Stedum
and Zeerijp wells where a temporary seismic array will be installed in autumn 2013.

Figure 9.12

Proposed improved shallow geophone network

The planned extension of the shallow geophone network (Figure 9.12) also aims to significantly reduce the
uncertainty in the position of the hypocenter. Moreover, it reduces the monitoring threshold for the whole
Groningen field to a M=0.5 level. More details on the geophone monitoring are provided in section 10.2.

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9.4

Geomechanical modeling

Currently, two types of modeling are on-going. One type has the objective to capture and represent the 3D
field conditions as good as possible. The other type evaluates the parameters to be incorporated in more
advanced modeling stages in the future. The first type is covered by the 3D modeling effort discussed in the
next section. The second type comprises 2D modeling studies that address the impact of the geometrical
configuration and the constitutive behavior of formations and faults on the onset of fault slip.
9.4.1

Full 3D modeling

The objective of the 3D geomechanical modeling approach is twofold:




Create a load-and-resistance modeling approach that characterizes Groningen subsurface behavior
related to production-induced fault reactivation
Evaluate how alternative production strategies may help to manage subsurface stresses, in order to
reduce fault slippage

The word “may” underlines the complexity of earthquake physics as well as the complexity of the
subsurface and its relevant parameters. This is discussed more thoroughly in section 9.1. Reactivation of
faults is linked to the 3D stress evolution in and around the producing field while it is being produced. Both
Baker Hughes and Exxon Mobil (XoM) were asked to build 3D models for the full field. In addition, XoM
also builds two zoom-in models that both capture more geological detail and a better mechanical
description for fault behavior. The location of the two zoom-in models is indicated in Figure 9.13.
220000

240000

260000

240000

260000

Legend
Induced earthquakes in The Netherlands
Magnitude
M < 1.5
1.5 <= M <= 2
2 < M <= 2.5
2.5 < M <= 3
3 < M <= 3.5

600000

580000

580000

600000

3.5 < M <= 4

0

10

20

Kilometres
220000

Figure 9.13

Location (red and grey rectangles) of the zoom-models from XoM.

The 3D modeling results are described in section 9.16 (3D modeling results). Figure 9.14 conceptually
illustrates how the geomechanical modeling may fit into a more comprehensive subsurface stress
management / risk management framework, also including lab measurements, field measurements and
surveillance.

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Figure 9.14

9.4.2

Geomechanical modeling as part of a subsurface stress management / risk management framework.

2-Dimensional modeling of fault slip

A 2-dimensional model generator is being developed to address some of the modeling uncertainties
highlighted in Section 9.2 above. This line of investigation builds on previous work by Roest (1993),
Mulder (2003) and Orlic (2012) among others. Main difference is the focus on the Groningen field
conditions. Both static and dynamic equilibrium conditions are being evaluated.
The main objective of the static evaluations is to develop a better understanding of the impact of
geometrical and constitutive modeling assumptions on the onset of fault slip for different depletion
conditions. In particular, the throw of formations with different thickness across a dipping fault is addressed
(Figure 9.15). The model allows incorporation of a different in-situ stress and pore pressure condition in
each formation on both sides of the straight fault plane. Also, the formation properties can be assigned
independently. Initially, the only failure mechanism incorporated is slippage along the fault plane assuming
no post-failure hardening or softening behavior. Subsequent evaluations will include more complex, nonlinear and time-dependent (creep) behavior for the fault as well as for the formations.
The dynamic (vibration) analysis is aimed to reveal the importance of inertia effects on the stress
distribution and the onset of fault slip.

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-2,700

-2,750

-2,800

-2,850

Depth [m]

-2,900

-2,950

-3,000

-3,050

-3,100

-3,150

-3,200

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

Fault shear capacity [-]

Figure 9.15

9.5

Example of a 2D finite-element model (left) and the Shear Capacity Utilisation (SCU) along a fault plane
(right) under increasing pressure depletion conditions. The fault is slipping where SCU=1.

2D Geomechanical investigation of the potential causes of fault slip and seismic events

The objective of the work reported in this section is to identify potential fault slip mechanisms and the
impact of various modelling options and their associated parameters on the simulated geomechanical
response. Knowledge of the potential fault slip mechanisms and slip geometries can help to focus the field
data acquisition programme and possibly test the actual occurrence of particular mechanisms in seismic
events. Knowledge about the simulated geomechanical response is expected to help selecting the most
appropriate modelling option for the observed behaviour. A two-dimensional modelling environment is
chosen to allow for a wide range of modelling options and parameters to be evaluated relatively quickly.
These modelling options comprise all main elements of the geomechanical evaluation framework as
outlined in Research study 5 (2013), namely geometrical modelling, constitutive modelling, in-situ stress
and depletion modelling, and also dynamic equilibrium modelling aspects.
This chapter reports the initial results of the 2D studies addressing the impact of formation offset on the
onset and post-failure behaviour of fault slip. Previous work by Roest & Kuilman (1994), Glab & van Eijs
(2001), Mulder (2003) and Orlic & Wassing (2012) has revealed that geometrical parameters and in
particular the offset along faults influences the onset of fault slip. Therefore, this study focuses on this
specific modelling aspect first. It differs from previous work in its evaluation of the depletion level at the
onset of fault slip for various offsets, but also the post-failure behaviour in terms of the development of total
length of the fault slip sections, the maximum Relative Shear Displacement and the dissipated energy level
as a function of depletion and fault offset. In addition to the fault throw aspect, also other geometrical,
constitutive and in-situ stress parameters will be investigated.
The main features of the newly developed 2D model generator are described, followed by a presentation of
the parameters used in this initial study. The geometrical parameters and in-situ stress conditions reflect the
Groningen field conditions as good as possible given the limited available data. The formation and fault
properties have been selected in such a way that fault slip is triggered within the depletion range applied
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(0-20 MPa), and such that the impact of fault throw is revealed without unnecessary influence of other
modelling aspects. Consequently, the reported values are not representative for the Groningen field
conditions at this stage, but are aimed to develop a qualitative insight into the impact of formation throw on
the onset of fault slip and slip behaviour.
9.5.1

Model features

A model generator has been built to evaluate the geometrical configuration outlined in Figure 9.16 using
finite-element techniques. The configuration comprises a dipping fault plane and 2 sets of horizontal
formation layers, 4 in the foot wall and 4 in the hanging wall. The most shallow layer (1) on each side of
the fault represents the overburden, layer 2 is a stiff anhydrite/carbonate interval, layer 3 represents a
potential reservoir unit that can be assigned a change of pore pressure, and the deepest layer (4) is the
underburden. Note that layer 2 represents the Basal Zechstein stratigraphic interval, and all Rotliegend
stratigraphic intervals (Ten Boer, Upper Slochteren, Ameland and Lower Slochteren) are grouped into layer
3. The thickness of each of the constituent model layers can be specified independently. The vertical throw
over the fault is defined by the difference in thickness of the overburden layer (1) in the hanging wall and
the foot wall. Furthermore, the dip angle and dip azimuth of the fault relative to North, as well as the
maximum horizontal stress azimuth is specified to set the desired initial stress conditions.

v (Depth)
Footwall



Displacement

 
 

h H and
azimuth
Ten Boer

Hanging wall

Slochteren

hh

Cm,h, νh, ΔPh

Cm,f, νf, ΔPf
hf

 


Dip angle

h H and
azimuth

C, 

Figure 9.16

Outline of the model and its main parameters.

The material properties, in-situ stress and pore pressure can be specified for each of the 8 layers
independently. The current model generator supports linear-elastic material behaviour, specified by
Young’s modulus and Poisson’s ratio. The total vertical stress is given by the density of each formation
layer, whereas both horizontal total stress components are specified through a k0 ratio with the total
vertical stress. The pore pressure is specified by a depth gradients from a given reference pressure and
reference depth. An independent change of pore pressure can be specified to 4 formation layers, namely
layers 2 and 3 on the hanging and foot wall side of the fault. This change of pore pressure is assumed to
be uniform within the 4 formation layers. The pore pressure in the fault can be assigned equal to the
formation pore pressure at either the left or right side of the fault.
The fault behaviour is described by a Mohr-Coulomb slip criterion, which is specified by a cohesion and
friction angle. The post-failure behaviour is assumed “ideal plastic”, which means that the shear stress
carrying capacity of the fault remains constant after onset of slip. A dilatancy angle is specified to describe
the post-failure behaviour in the fault [TNO-DIANA, 2011].
Fault slip behaviour is evaluated by a 2-dimensional (DIANA) plain strain finite-element model (Figure
9.17). One-dimensional interface elements simulate the behaviour of fault slip. The size of the interface
elements is restricted to 1 m in order to appropriately describe the development of critical stress conditions
in the presence of fault offset. The quadrilateral continuum elements next to the interface elements have a
similar size and increase towards the boundaries of the model. The fault zone and the four formations on
each side are recognised by the small element region in the middle of the model in Figure 9.17.

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Left

Right





Figure 9.17

Example a generated finite-element mesh with down-thrown (positive offset) model layers on the lefthand side of the fault.

M2

M1
M18

mFS7_Fault_38

M67

MFS7_Fault
40/41

M69

INT_8
mFS11_Fault_14

M39

Figure 9.18

Top Rotliegend depth map and the interpreted fault planes [2] in the North-Western part of the
Groningen field. A selection of faults in the vicinity of the largest recorded seismic events has been
indicated.

Geometrical setting
The analysis results reported in this study have been obtained from a model with a total width of 2000 m
and a depth between 0 and 6000 m. The thickness of layers 2 and 3 as described above has been derived
from the Groningen subsurface model.

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The fault configuration of fault M1 (Figure 9.18) has been used to determine the ranges for various
geometrical parameters in the current study. The various relevant parameters are summarised in Table 9.1.
The thicknesses of all model layers have been kept constant in the current study, except for the overburden
thickness on the left-hand side of the fault (see Figure 9.17). In this way, throw magnitudes between -300
and +500 meter have been prescribed (Note that only normal faulting - i.e. positive throw - is seen in the
Groningen field). The fault dip has been set to 65 degree with the horizontal (25 degrees with the vertical).
This configuration generates a modelling cross-section perpendicular to fault M1 and looking in NorthNorth-Western direction.
Unit

Value

Rotliegend depth

[m]

2780

Basal Zechstein thickness

[m]

50

Rotliegend thickness

[m]

215

Vertical throw

[m]

Between -300 and +400

Fault dip with horizontal

[deg]

65

Fault sip azimuth North to East

[deg]

240 (60)

Table 9.1

9.5.2

Formation thickness, throw and other geometrical properties along Fault M1.

In-situ stress and formation properties

The in-situ stress has been taken from [Van den Bogert et al., 2013]. The stress gradients have been
converted into a formation density and a maximum and minimum total horizontal stress ratio to the total
vertical stress (k0-max and k0-min respectively) to facilitate incorporation into the 2D model. The results of
the conversion are given in Table 9.2.
Identical values for Young’s modulus and Poisson’s ratio have been used for all model layers in order to
remove any influence of stiffness contrast on the onset of fault slip. The impact of stiffness contrasts is to be
addressed in further studies to better represent the actual values in the field.
Table 9.2

Formation thickness, throw and other geometrical properties along Fault M1.

Density

K0-max

K0-min

Young’s
modulus

Poisson’s
ratio

Unit

[kg/m3]

[-]

[-]

[GPa]

[-]

Layer 1 – Overburden

2172

0.795

0.748

10

0.25

Layer 2 – Basal Zechstein

2450

0.795

0.748

10

0.25

Layer 3 – Rotliegend

2450

0.795

0.748

10

0.25

Layer 4 – Underburden

2700

0.795

0.748

10

0.25

The initial pore pressure in the overburden layer 1 is hydrostatic with a pressure gradient of 10 kPa/m (1.0
bar/10m). Also, layer 2 has been assigned this initial pressure gradient. The more saline aquifer in the
underburden layer (4) causes a somewhat elevated pressure gradient at 11.66 kPa/m. In model layer 3 a
gas density of 1.8 g/cm3 has been applied. Furthermore, the Gas-Water Contact has been set at 2995 m
TVD at a pressure 35.2 MPa (352 bar). This pressure regime has been applied to layers 3 and 4.
Finally, cohesion and friction angle for the interface elements that describe the fault behaviour are set to 7
MPa and 13 degrees, respectively. These values have been used in a previous study to ensure that onset of
fault slip is predicted in a wide range of values for Poisson’s ratio that strongly influences the change of the
horizontal stress in uniaxial compaction conditions under reservoir depletion.

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Reservoir depletion conditions
Uniform reservoir depletion has been simulated by lowering the pore pressure in model layer 3 on both
sides of the fault. That is, the fault has been assumed to transmit and to equilibrate any pressure difference
that might occur in the course of production. The pore pressure in the fault follows the depletion along those
parts that are in contact with a depleting layer and maintains the virgin pressure in all other parts.
Depletion has been increased in 20 steps of 1 MPa (10 bar). This is the depletion level that is encountered
in large part of the field, and has also been used in a previous assessment [Van den Bogert et al., 2013].
No pressure depletion is prescribed in any of the other model layers.
9.5.3

Model results

The model described in the section 9.5.1 has been used to evaluate the onset of fault slip and subsequent
slip behaviour under increasing reservoir depletion conditions for a throw between -300 (up-thrown) and
+500 m. About 40 analyses have been conducted with throw increments of 20 meter.
The results of the two-dimensional analyses are discussed in detail for the case with a (downward) throw of
80 meter. The results include the prescribed pore pressure, the calculated effective normal and shear stress,
the Shear Capacity Utilisation (SCU) and the Relative Shear Displacement (RSD) along the fault plane as a
function of depth and depletion level. Furthermore, the strain-energy level in the model is tracked as well as
the energy dissipated by fault slip. It is emphasised that none of the mentioned results are representative for
the Groningen field conditions at this stage. The analyses are aimed to develop a qualitative insight into the
impact of formation throw on the onset of fault slip and slip behaviour. Various modelling parameters have
been selected such that fault slip would be triggered.

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-2,500

-2,500

Virgin

Depleted (-5/-5)
-2,600

-2,600

Depleted (-10/-10)
Depleted (-15/-15)

-2,700

-2,800

-2,800

-2,900

Depth [m]

-2,900

Depth [m]

Depleted (-20/-20)

-2,700

-3,000

-3,000

-3,100

-3,100

-3,200

-3,200

-3,300

-3,300
Virgin

Depleted (-5/-5)
-3,400

Depleted (-10/-10)

-3,400

Depleted (-15/-15)
Depleted (-20/-20)

-3,500
-0.25

-3,500
0

20

30

40

50

60

-2,500

-2,500

-2,600

-2,600

-2,700

-2,700

-2,800

-2,800

-2,900

-2,900

-3,000

-3,100

-3,200

-3,200

-3,300

-3,300

-0.10

-0.05

0.00

0.05

1.20

1.40

Fault shear displacement [m]

Virgin

Normal stress (Virgin)
Normal stress (-10/-10)
-3,400
Normal stress (-20/-20)
Shear stress (-5/-5)
Shear stress (-15/-15)
-3,500
-50
-40
-30

Figure 9.19

-0.15

-3,000

-3,100

c)

-0.20

b)

Pore-pressure [MPa]

Depth [m]

Depth [m]

a)

10

Normal stress (-5/-5)
Normal stress (-15/-15)
Shear stress (Virgin)
Shear stress (-10/-10)
Shear stress (-20/-20)
-20

-10

Fault effective stress [MPa]

0

Depleted (-5/-5)
-3,400

Depleted (-10/-10)
Depleted (-15/-15)
Depleted (-20/-20)

-3,500

0.00

10

d)

0.20

0.40

0.60

0.80

1.00

Fault shear capacity [-]

a) Pore pressure, b) Relative Fault Shear Displacement (RSD), c) Effective normal and shear stress, and
d) the Fault Shear Capacity Utilisation as a function of the depth for a throw of 80 meter, under virgin
and 5, 10, 15 and 20 MPa depletion conditions.

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Figure 9.19a shows the applied pressure profile as a function of depth for 5, 10, 15 and 20 MPa (50,
100, 150 and 200 bar) reservoir depletions. The location of the interfaces between formation layers 1, 2,
3 and 4 on both sides of the fault are indicated by horizontal (green and blue) marker lines. The gas
gradient in the reservoir formations is steeper than the water gradients in over- and underburden.
Figure 9.19b shows the Relative Shear Displacement (RDS) along the fault plane as a function of depth for
the same depletion levels. Negative values indicate that the left-hand side of the fault is displaced
downwards relative to the right-hand side of the fault. For a throw of 80m a maximum RSD of 0.10 meter
is calculated under 20 MPa depletion, whereas the slip is negligible for a depletion level of 10 MPa and
lower.
Figure 9.19c shows the effective normal and shear stress distribution and Figure 9.19d shows the derived
Shear Capacity Utilisation (SCU) as a function of depth. This is the ratio of the actual shear stress  and the
shear stress carrying capacity max according to a Coulomb friction law, with a cohesion of 7 MPa and a
friction angle of 13 degree. An SCU of 1 means that the maximum shear stress has been reached and that
the fault is slipping. Values larger than 1 are not possible. The total length of the interface elements for
which SCU=1 is defined as the slip section. Two slip sections are found under 5, 10 and 15 MPa depletion,
whereas one larger slip section is found under 20 MPa reservoir depletion.

Onset of fault slip
20

18
16

Depletion [MPa]

14
12

10
8

6
4

2
0
-400

Figure 9.20

-200

0
200
Formation Throw [m]

400

600

Onset of fault slip as a function of fault throw in the formation

The onset of fault slip is determined by the occurrence of an interface element with an SCU of 1, and is
given as a function of fault throw in Figure 9.20. The depletion level is calculated by interpolation between
depletion step results. It is seen that a fault with zero throw requires the largest depletion level for the onset
of fault slip, whereas a fault with a throw that is equal to the reservoir thickness (215 m in this case) starts
to slip at the smallest depletion level. Furthermore, it is found that extremely large positive or negative
throws converge to the same depletion level for the onset of slip. These cases describe a model
configuration with depletion on one side of the fault only. The offset of the depleting reservoir layer is so
large that peak stresses do not interfere with each other. Finally, it is noted that fault slip is not initiated
under 20 MPa depletion in the case of 0 m offset. In this case, the maximum SCU is 0.97 under 20 MPa
depletion.

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350

0m offset
80m offset

Total length of Slip section(s) [m]

300

215m offset
300m offset

250

Merging of two slip
sections into one

200

150

100

Merging of two slip
sections into one

Single slip section
after onset of fault slip

50

0
0

2

4

6

8

10

12

14

16

18

20

Depletion [MPa]

Figure 9.21

Development of the slip section [m] as a function of the depletion level for different throws (offset) along
the fault plane.

The development of the slip section with increasing reservoir depletion is plotted in Figure 9.21 for the
cases of 0, 80, 215 and 300 m throw along the fault. No fault slip is developed with zero throw up to 20
MPa depletion, while the slip section gradually grows starting after a depletion slightly over 4 MPa in the
case of 215 m fault throw. This case features only one slip section that initiates at the top of the downthrown block. The case of 80 m fault throw, shown in detail in Figure 9.19, features two slip sections up to
12 MPa depletion when both merge into a single slip section. Figure 9.21 shows that the growth of the two
slip sections accelerates with depletion, until they merge and continue to grow in a stable rate upon further
depletion. The same fault slip geometry is found for a fault throw larger than the thickness of the depleting
reservoir formation, in which case two slip sections merge into a one larger section under increased
depletion.
350

Total length of Slip section(s) [m]

300
250

3 MPa

Reservoir thickness 215 meter
Fault dip 65 degree
Fault dip azimuth 60/240 degree
SHmax azimuth160 degree
Homogenous formation properties

4 MPa
5 MPa
6 MPa
7 MPa



200

8 MPa



9 MPa
10 MPa

11 MPa
12 MPa

150

13 MPa
14 MPa
15 MPa

100

16 MPa

50
0
-300





17 MPa



18 MPa
19 MPa
20 MPa

-200

-100

0

100

200

300

400

500

Formation Throw [m]

Figure 9.22

Total length of slip sections as a function of fault throw and reservoir depletion.

Figure 9.22 shows the total slip length for all evaluated depletion levels (starting at 3 MPa) and a throw
between -300 and +500 m. Three fault slip conditions can be identified based on throw and depletion
level:
1. Two slip sections are found in case of a positive reservoir offset and under low to moderate depletion
levels, labelled 1 and 2.
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2. A single, merged fault slip section is found under in case of a positive reservoir offset and under
elevated depletion levels, labelled 3 and 4.
3. Two slip sections that do not merge even under elevated reservoir pressure in case of a negative
reservoir offset, labelled 5.
The merging of the two slip sections into one is dependent on the offset along the fault. The transition from
slip condition 1 with two slip sections to slip condition 3 with one slip section is somewhat different for a
throw smaller than the depleting formation thickness (215 m in this case) than the transition from slip
condition 2 to 4 for larger throws. In the latter case, the slip section length grows more rapidly with
depletion. This is also seen in Figure 9.21, where the merged slip section for a throw of 300m increases
faster (after 14 MPa depletion) compared to the case of a throw of 80 m (depletion larger than 12 MPa).
-0.200

Maximum Reslative Shear Displacement [m]

-0.180
-0.160
-0.140

3 MPa

Reservoir thickness 215 meter
Fault dip 65 degree
Fault dip azimuth 60/240 degree
SHmax azimuth160 degree
Homogenous formation properties

4 MPa
5 MPa

6 MPa
7 MPa
8 MPa

-0.120

9 MPa

-0.100

11 MPa

10 MPa
12 MPa

-0.080



-0.060

13 MPa



14 MPa

15 MPa

-0.040

16 MPa

-0.020



0.000
-300

Figure 9.23

-200

-100

0

17 MPa





100
Formation Throw [m]

200

300

18 MPa
19 MPa

400

20 MPa

500

Maximum Relative Shear Displacement [m] as a function of throw and reservoir depletion

The maximum Relative Shear Displacement (RSD, Figure 9.23) shows an extreme value at an offset slightly
smaller than the depleting formation thickness for all depletion levels. The five different fault slip conditions
indicated in the figure highlight that the RSD accelerates with depletion after the slip sections have merged.
This particularly holds for fault throws smaller than the thickness of the depleting reservoir: the maximum
RSD in slip condition 1 is smaller than in condition 2 under the same depletion level, while the maximum
RSD in 3 is comparable to, or larger than, in 4.
In sum, it is seen that fault configurations with an offset smaller than the thickness of the depleting reservoir
layer develop a larger slip displacement over a shorter slip length than configurations with an offset larger
than the thickness of the depleting reservoir layer.
16000

160
Formation energy
Fault energy

140

12000

120

10000

100

8000

80

Merging of two slip
sections into one

6000

60

4000

40

2000

20

0

Fault Energy [MNm]

Formation Energy [MNm]

14000

0

0

Figure 9.24

2

4

6

8
10
Depletion [MPa]

12

14

16

18

20

Formation and fault energy as a function of depletion level for a throw of 80 m.

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The dissipated energy by fault slip is calculated as the product of the shear stress, the slip displacement and
the area of each interface element. The dissipated fault energy (Figure 9.24) shows an acceleration with
increased depletion after the two slip sections have merged. Note that the onset of fault slip, which occurs
at 7.3 MPa depletion in the case of 80 m throw, is not visible on the scale used in Figure 9.24, while the
merging of the two slip sections is. The energy dissipated in the fault up to onset of slip is elastic and
recoverable, whereas the incremental energy after onset of slip is not. However, the dissipated energy level
remains limited as long as two slip sections exist.
The strain energy in the formation increases nonlinearly with depletion and is several orders of magnitude
larger than the energy dissipated by fault slip. The strain energy is calculated by integration of the stressstrain relationship for all material points in the 2D model. A larger model (volume) will have a larger
strain-energy level while the dissipated fault energy will remain the same in that case. For the linear-elastic
stress-strain relation employed here, the incremental strain-energy stored in the model by an incremental
depletion of the reservoir pressure increases with the depletion level and causes the nonlinear curve in
Figure 9.24 for the formation energy.
250

Fault Energy [MNm]

200

10 MPa

Reservoir thickness 215 meter
Fault dip 65 degree
Fault dip azimuth 60/240 degree
SHmax azimuth160 degree
Homogenous formation properties

11 MPa
12 MPa
13 MPa
14 MPa

15 MPa
150

16 MPa
17 MPa



100

18 MPa



19 MPa
20 MPa

50

0
-300

-200



-100

0




100

200

300

400

500

Formation Throw [m]

Figure 9.25

Fault energy dissipated by fault slip as a function of throw and depletion level

The five different fault slip conditions indicated in Figure 9.25 highlight the limited energy dissipation for
conditions 1, 2 and 5. For slip conditions 1 and 2, the energy dissipation in the latter is substantially larger
for offsets larger than the thickness of the depleting layer. In both cases 1 and 2, the energy dissipated by
fault slip becomes more significant when the two slip sections have merged into one slip section, indicated
by the labels 3 and 4 in the figure. It is seen that most energy is dissipated in slip condition 3, which is in
general characterised by the larger RSD and smaller slip section length than in slip condition 4.
9.5.4

Summary of findings 2D modeling

In the previous section, different fault slip geometries have been identified based on fault throw and
depletion level under the assumptions given in section 9.5.1. It is found that offset of depleting reservoir
layers along existing faults highly impacts the geomechanical response in terms of







The depletion level at the onset of fault slip
The number of slip sections
The total length of the slip sections
The maximum Relative Shear Displacement
The energy dissipated by fault slip.

The following cases can be identified in terms of throw:
1. The throw is smaller than the thickness of the depleting reservoir layer
2. The throw is larger than the thickness of the depleting reservoir layer
3. Negative throw (reverse faulting).

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Fault slip generally starts with two slip sections at a relatively low depletion level in all three cases. They
differ in the merging of the two slip sections at an elevated depletion level for cases 1 and 2, which is not
found for the evaluated condition in case 3. The merging of the two slip sections has different
characteristics in Case 1 than in Case 2. Generally, in Case 2 a smaller slip displacement is developed
over a larger slip length compared to Case 1. Also, the energy dissipation varies. The two slip sections in
Case 2 dissipate more energy than the two slip sections in Case 1 under the same depletion level, while this
is the other way around for most throws in Case 1.
Two exceptions to the above are identified, where a single (merged) slip section occurs directly after the
onset of fault slip, namely
1. Zero throw:
a single slip section is predicted over the entire thickness of the depleting reservoir layer just after
the onset of fault slip.
2. The throw is equal to the thickness of the depleting reservoir layer, in which case the slip section
gradually increases with depletion at the location where the two depleting layers connect.
These two exceptions also provide the upper and lower limit of depletion at the onset of fault slip. Exception
case 1 can hold the highest depletion level before the onset of fault slip takes place, while Exception case 2
starts to slip at the lowest depletion level. Hence, onset of fault slip is found to be strongly dependent on
throw.
Under the current modelling assumptions, the development of the slip section after the onset of fault slip
occurs most gradually with depletion for a fault throw equal to the thickness of the depleting reservoir layer.
For all other cases with normal faulting, the development of the slip section accelerates with depletion until
the two sections merge into a single slip section (Figure 9.21). In the extreme case, in the absence of throw,
the slip section develops abruptly at the onset of fault slip. So, fault slip in the two extreme cases occurs
either abruptly over the thickness of the depleting layer at an elevated depletion level, or gradually with
depletion starting at a low depletion level.
The assumption of ideal plastic fault slip behaviour causes a stable propagation of the slip sections in all
cases evaluated so far. Faults with zero offset show the most rapid development of the slip section, but also
in this case a stable equilibrium condition is found. This implies that incremental reservoir depletion is
required to propagate the slip section and, as a result of the time-scale of this process, that this occurs in an
a-seismic fashion. So, it is found that an ideal plastic fault slip model in combination with the fault
configurations evaluated so far, does not lead to seismogenic fault slip.
The current results are in line with those reported earlier by Mulder (2003) and Orlic & Wassing (2012).
The calculated Relative Shear Displacement (RSD) shows a similar distribution as a function of depth,
specific fault throw cases are recognised, and the maximum RSD is in the same range as in Mulder
(between 0.15 and 0.20 m). A more comprehensive insight is obtained in the development of merged slip
sections at elevated depletion levels and their dependence on fault throw. Furthermore, a relationship is
observed between reservoir depletion, fault throw, number of slip sections and the total length of the slip
section, the maximum RSD, and the dissipated energy.
Although only one parameter has been evaluated so far, it is clear that the structural geometry is an
important element in the geomechanical explanation of fault slip. Further analysis of sensitivities to other
geometrical factors, such as reservoir thickness, fault dip and dip azimuth angle, and formation and fault
properties should reveal their relative importance. The available structural model [Visser, 2012] provides a
basis for further constraining the geometrical parameters. More accurate determination of the hypocentre
location of future seismic events may also help to constrain the modelling parameters for formation and
fault behaviour.
9.5.5

Next steps 2D modeling

Despite the two-dimensional nature of the model, some relevant insights have been gained into the
potential fault slip geometries that might be responsible for the recorded seismic events in the Groningen
field. It is expected that the various fault slip responses found in the current study will occur under different
depletion levels, or may be suppressed or emphasised by other factors not considered here. Also, other
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responses may be found when considering other modelling options. Nevertheless, the study shows that
different fault slip geometries are conceivable, and that each of these is characterised by a different
depletion triggering level, a different slip section length, maximum RSD and different fault slip energy
dissipation. This opens up the opportunity to look for seismic fingerprints to
1. Determine if slip along existing faults is indeed the root cause of the recorded seismic events, and
2. Discriminate between different slip geometries, constrained by available structural information and
the analysis of focal mechanisms from recorded events.
Further studies will be aimed at developing a more comprehensive overview of potential fault slip
mechanisms and geometries and their characteristics under various modelling assumptions. Initial focus will
be on other geometrical aspects, such as reservoir thickness, fault dip angle and dip azimuth angle,
because these parameters are relatively easy to quantify. Furthermore, the impact of fault slip softening will
be addressed to investigate under which conditions potentially unstable seismogenic fault slip conditions
can be developed. Finally, the impact of stiffness and (horizontal) in-situ stress contrasts will be addressed
using the current 2D-model.
Based on the results of these additional studies, an attempt could be made to incorporate the 2D
configurations in the 3D structural model of the Groningen field. Also, an update of the probabilistic
hazard assessment and the 3D geomechanical field model could be considered. How exactly, and to what
extent, is best decided after completion of the 2D modelling studies indicated above.
The current linear-elastic formation behaviour in the 2D model generator can be extended to include
nonlinear and time-dependent behaviour. This would allow more appropriate modelling of salt behaviour
and the impact on onset and post-failure behaviour of fault slip.
In an ideal case, the relevance of identified slip geometries and associated modelling assumptions and
parameter values will be confirmed by field data. Whether or not future monitoring and data acquisition
will provide sufficient information to do so will remain uncertain.

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9.6

Three-dimensional modelling results

9.6.1

Geomechanical assessment of seismicity with full field geomechanical model (URC)

Full-field 3D finite element models are utilized to improve the understanding of geomechanical aspects
during seismic events in the Groningen field and evaluate alternate producing strategies that may help
manage subsurface stresses to reduce fault slippage. This multi-scale modeling framework includes a
“global model” to capture full-field phenomena and “sub-models” which honor conditions of the global
model, but also include explicit modeling of selected faults to understand production-induced fault slippage.
Two key sub-model regions are considered in this study (Figure 9.26).

Figure 9.26

Site location showing epicenters of registered earthquakes and location of sub-models (dashed
rectangles); sub-models were developed for regions with varying levels of historical seismicity

This modeling framework requires the use of state-of-the art computational mechanics implicit algorithms to
capture fault slip behavior with non-linear contact mechanics. The modeling framework is illustrated in
Figure 9.27.

Figure 9.27

Multi-scale geomechanical modeling framework. Reservoir pressures from reservoir simulator output are
mapped into geomechanical models as boundary conditions; deformation of sub-model boundaries is
driven by deformation of the global model. Global model is 182.8 km x 190.5 km in plan view;
additional details are discussed in section Error! Reference source not found..

The geomechanical models integrate geologic, geomechanics, drilling, and production data to simulate
field deformation from the start of production. These advanced models consider irregular geologic layering
and fault surfaces, variable rock properties including Biot’s coefficient effects for reservoir layers, a ratedependent salt model to capture creep and stress relaxation effects before and during production, poromechanics elements to capture non-uniform pressure changes throughout the field over time, a frictional
law to model slip behavior along faults, and other input data such as initial stresses to represent the virgin
in-situ stress condition. Additional information is provided in Section 11 Appendix A.
Results from the global model provide a reasonably good match to field measurements of surface
subsidence and downhole strains (De Hond well) as shown in Figure 9.28.

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Figure 9.28.

Figure 9.28

Validation of geomechanical model: a) 2012 subsidence in meters (m), b) Predicted subsidence vs. field
measurement; subsidence measured between the year of first leveling measurement (different for
different locations) and 2012, and c) Strain predictions compared to downhole strain measurements
derived from radioactive bullets at De Hond well.

Deformation of the models is driven by reservoir pressure changes and salt creep behavior. Displacements
from the global model are mapped into the boundaries of the sub-models to enable calculations of stresses
on faults and resulting slip at different faults and at different points in time (Figure 9.29).

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Figure 9.29

Example results for global model and sub-models 1 and 2; year 2012. East-West horizontal
displacement contours (U1) are shown for top of Unit 14 (USS_2_Res). Predicted fault slippage is
significantly more in sub-model 1 than in sub-model 2.

Seismicity at Groningen has been linked to production-induced reactivation of existing faults at the
reservoir level [Dost et al, 2012]. The fault slip predicted in the sub-models, may in reality occur
aseismically, as a seismic event, or as a combination of the two. Aseismic fault slip does not contribute to
the generation of seismic waves and therefore to radiated energy. This modeling framework presents
significant improvements over analytical or numerical models that cannot predict relative displacement
along fault surface, but they are still limited to a quasi-static setting with the consequence that radiated
energy cannot be explicitly accounted for. Still, the onset of aseismic and seismic slip can be captured by
the same frictional slip criterion. For this reason it is critical to understand and manage subsurface stresses
to reduce fault slippage that may lead to seismic events.
Different quantities may be used to evaluate subsurface stress conditions and fault activity for different
production scenarios. The Shear Capacity Utilization (SCU) is a scalar quantity which expresses the
criticalness of fault stress state relative to the fault strength. This quantity is particularly useful for a quick
stability analysis in the absence of slip information. However, since it does not contain slip or post-failure
information, it cannot discriminate between significant and small slip events. Fault slippage, which is
defined as the relative shear displacement on a fault surface, may provide relevant information. Slip data
at a single location is of limited use as this information does not indicate whether the slip is restricted to a
narrow or wide area. Analyzing fault slip at different locations and points in time can become rather
complex and have limited usefulness. The calculated dissipated energy during frictional sliding is a scalar
quantity that provides a representative measure of fault activity for a given area of interest and can be used
to identify production scenarios that reduce fault loading. This quantity integrates key geomechanical
information: fault shear stress and strength, fault slippage, and the area undergoing frictional sliding.
While these geomechanical models cannot explicitly predict radiated energy, model results show that a
fraction of the cumulative dissipated energy correlates well with the cumulative radiated energy from
observed seismic events for sub-model 1 and 2 regions (Figure 9.30). Hence, the calculated dissipated
energy from fault slippage appears to be a suitable indicator of overall seismic activity. Sub-model results
are in agreement with field observations, indicating that total dissipated fault energy in sub-model 1 region
is significantly greater than the one predicted for sub-model 2 region (Figure 9.30). The Gutenberg and
Richter (1956) relation log_10⁡E=1.5M+4.8 is used to estimate the radiated or seismic energy E in Joules
during earthquake events based on the magnitudes M published by KNMI.
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Figure 9.30

Cumulative dissipated fault energy (sub-model results) vs. cumulative radiated energy (field data) for
sub-models 1 and 2 (SM1 and SM2) as a function of time. Results are normalized to 1996 representing
the first year with a complete earthquake catalog. Left vertical axis corresponds to dissipated energy
and right vertical axis to radiated energy; vertical axes are related through the same scaling factor for
both plots.

Moreover, the scaling factor between radiated and dissipated energy considered in Figure 9.30 is the
same for both, sub-model 1 (SM1) and sub-model 2 (SM2). These results indicate that advanced
geomechanical models are a powerful tool that can provide valuable insight into the overall trend of
cumulative radiated energy. It is important to note that it is not possible to translate dissipated energy from
fault slip into earthquake magnitudes, because of the quasi-static nature of the models. Fault slip may or
may not be accommodated in a seismic event. Here, it is found that this ratio is similar between two areas
with a different seismic activity level. Even though there is no reliable predictor for individual earthquake
events, this full field geomechanical modeling approach should be useful in understanding Groningen
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seismic activity and to identify production scenarios that reduce fault loading and therefore mitigate seismic
activity.
Several depletion scenarios have been developed (see Table 3.2) and are being evaluated with the
modeling framework discussed herein (Figure 9.31 and Figure 9.32). The production scenarios consider
two geologic interpretations: i) moderate NW aquifer support (G1), and ii) and weak NW aquifer support
(G2). Three development scenarios: i) KHM1 hook-up, 2nd and 3rd stage compression (D1), ii) D1 + NW
periphery development (D2), and iii) D2 + SW periphery development (D3). And the following curtailment
conditions: i) market demand (C1), ii) 40 bcm/year cap (C2), and iii) 30 bcm/year cap (C3). Depletion
scenarios
considered
in
this
section
pertain
to
geologic
interpretation
G1.

Figure 9.31

Cumulative dissipated fault energy vs. time for selected production scenarios in SM1 region. Curtailing
production (C2 and C3) has a limited effect in the total dissipated energy; additional development in the
SW (C1-D3) has a limited effect in the SM1 region compared to C1-D2; production curtailment to 30
bcm/yr (C3-D1) reduces dissipated energy, however additional development in the northwest region
(C3-D3) increases dissipated energy closer to the base case (C1-D1).

Figure 9.32

Cumulative dissipated fault energy vs. time for selected production scenarios in SM2 region. Additional
development in the NW (C1-D2) has no practical effect in SM2 region. Additional development in the

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SW (D3) has a moderate effect in SM2 region. Curtailment scenario C2 shows a minor decrease on
dissipated energy relative to market demand case C1.

Figure 9.33

Cumulative dissipated energy vs. volume for selected production scenarios in SM1 region. Minor
cumulative dissipated energy differences are observed across the investigated depletion scenarios. Right
vertical axis corresponds to cumulative radiated energy by year. Results are normalized to 1996.

Figure 9.34

dissipated fault energy vs. volume for selected production scenarios in SM2 region. Dissipated energy is
greater for depletion scenarios D3 (additional development in the SW periphery) when total production
is more than 2,500 bcm. Right vertical axis corresponds to cumulative radiated energy by year. Results
are normalized to 1996.

Figure 9.33 and Figure 9.34 show dissipated energy evolution as a function of total produced volumes for
both sub-model regions. The production alternatives investigated so far do not show significant changes in
the dissipated energy relative to the base case (G1-C1-D1). Model results indicate that curtailment of
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production is not an effective alternative for mitigating fault energy dissipation and related fault activity in
sub-model 1 and 2 regions (Figure 9.33 and Figure 9.34).
So far we have considered creep behavior for the irregular Zechstein salt section located above the
reservoir. To better understand the effect of salt creep behavior in the reservoir faults, we also model the
stress-strain response of the salt section with a linear elastic rate-independent constitutive model. This
sensitivity analysis indicates that salt creep behavior reduces overall fault loading in SM1 and SM2 regions
(Figure 9.35). Rate-dependent salt behavior affects nearby stresses and deformation mainly due to two
mechanisms: i) the inability of creeping salt to sustain deviatoric stresses with time affects the stresses in the
surrounding sediments according to the global salt geometry and local irregular configuration, and ii) the
more compliant viscous salt behavior reduces the constraint on the lateral deformation of
compacting/contracting rocks subjected to depletion. Note that the constant scaling factor between
radiated and dissipated energy for the two sub-model regions was developed using the results with salt
creep included. This factor may be different if salt creep effects are not considered.

Figure 9.35

Cumulative dissipated fault energy as a function of time for SM1 and SM2 showing the effect of creep
behavior in the Zechstein salt section. Total fault dissipated energy is reduced when salt creep behavior
is considered.

Results presented in this section demonstrate that an advanced understanding of fault reactivation and
related seismicity at Groningen is achieved with an improved modeling framework. At the same time, the
findings given here leave room for extension, both in analysis and in modeling. Enhancements to the
Coulomb friction law are required to explain other specific features of faulting mechanics such as slip and
velocity weakening. However, this potential extension would require additional data that is not currently
available, and would result in more complex models, making the analysis even more difficult. Additional
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geologic and geomechanical information could also be incorporated into the model. Some of these
improvements may be considered in future work based on Groningen Project needs and feasibility
constraints imposed by current computational capacity. Finally, our models should be further validated as
additional seismic field monitoring data becomes available.
In summary, detailed finite element models provide significant insight into the mechanics of a very complex
problem. The mechanics of fault behavior considered in the models described above can only be
investigated by numerical modeling of a complex non-linear boundary-value problem that captures
discontinuous displacement fields across fault surfaces. Additional sub-models may be developed to cover
the entire field region to capture the full impact of alternative production scenarios. These models are
currently being used to test a range of production scenarios that may potentially be implemented to better
manage subsurface stresses that could lead to fault slippage and seismicity. They may also be used to help
in optimizing the design of surveillance programs. The expectation is that insight gained from advanced
geomechanical modeling is used to create alternative production scenarios to effectively reduce fault
instability, and that the capability to test alternate producing strategies is used to provide an actionable
dimension to an overall Risk Management Framework for Groningen.
9.6.2

Geomechanical assessment of seismicity with full field geomechanical model

Production-induced pore pressure changes are responsible for rock stress variations which can trigger fault
slip and radiate seismic energy. Pressure maintenance is proposed as an alternative measure to mitigate
fault reactivation and related seismicity. The multi-scale geomechanical framework presented in section
9.6.1 is utilized herein to evaluate the fault energy dissipation of sub-model 1 and 2 regions (NW-central
and SW central regions respectively) when subjected to a production scenario involving Nitrogen injection.
The predicted dissipated energy for this injection case is compared to selected production depletion cases
for SM1 and SM2 regions in Figure 9.36 and Figure 9.37 respectively. This particular injection scenario
provides enough pressure support in SM1 region to practically stop fault energy dissipation after 2019.
Fault energy dissipation resulting from this injection test case is slowed in SM2 region but not stopped,
indicating partial pressure support.

Figure 9.36

Cumulative dissipated fault energy vs. time for selected production scenarios in SM1 region. Depletion
cases show a monotonic increase in dissipated energy with time. The Nitrogen injection case (triangular
symbols) considered in this analysis provides sufficient pressure support in SM1 region to stop energy
dissipation after 2019.

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Figure 9.37

Cumulative dissipated fault energy vs. time for selected production scenarios in SM2 region. The
Nitrogen injection case (triangular symbols) reduces fault loading and mitigates fault energy dissipation
after 2019. Note that pressure support from this injection scenario in SM2 is not as effective as in SM1
region.

The dissipated energy plotted vs. cumulative production volumes are shown in Figure 9.39 and Figure
9.40. Note that the injection-driven pressure support in SM1 region results in a negligible increase in
surface subsidence (predicted by global model analysis, shown in Figure 9.38 once injection is
implemented. This test case, injection for pressure support, once implemented and assuming it’s
implemented consistently across the field, could arrest compaction and therefore arrest seismicity. Other
production strategies short of full injection support may also balance stresses on faults and arrest seismicity,
even if compaction is not fully arrested. Such opportunities, represented by the “working space” in Figure
9.39 and Figure 9.40 wherein the dissipated energy vs. production volume lies between the level in 2012
and the forecast for case without any curtailment, are being explored via 3D geomechanical modeling.
Further modeling may help in developing production scenarios with best combination of uncurtailed
production and seismicity reduction.

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2012

2020

2049

G1-C1-D1

G1-Nitrogen
Injection

(After start of nitrogen injection)

Figure 9.38

Comparison of surface subsidence (m) for base case G1-C1-D1 with that for nitrogen injection; note
negligible change in surface subsidence for nitrogen injection case after start of injection compared to
the subsidence for base case over the same period

Figure 9.39

Cumulative dissipated fault energy vs. cumulative production volume for selected depletion scenarios
and a Nitrogen injection case (triangular symbols); SM1 region. “Working space” denote region
bounded by uncurtailed depletion and full pressure support end members. Results correspond to a fault
friction coefficient of 0.48. Results are normalized to 1996 representing the first year with a complete
earthquake catalog.

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Figure 9.40

Cumulative dissipated fault energy vs. cumulative production volume for selected depletion scenarios
and a Nitrogen injection case (triangular symbols); SM2 region. “Working space” denote region
bounded by uncurtailed depletion and full pressure support end members. Results correspond to a fault
friction coefficient of 0.48. Results are normalized to 1996 representing the first year with a complete
earthquake catalog.

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9.7

Conclusions

Our understanding of seismicity and the geomechanical behavior of faults in the Groningen area and their
relation to seismic events is hampered by the dimensions and complexity of the Groningen structure and the
scarcity of quantitative data on key geomechanical parameters. A twofold approach is taken to overcome
these limitations. First, the acquisition of new data has been intensified (see Chapter 10.2). Second, new
insights will be gained through the application of advanced 2D and 3D geomechanical modeling
techniques. Both approaches are ongoing, as discussed elsewhere in this document.
In summary, the following modeling uncertainties need to be further addressed, and associated monitoring
and measurement activities to be carried out:










The minimum horizontal stress is poorly constrained. No reliable data is available from the time before
production started, while limited data is available under depleted reservoir conditions. Stress
anisotropy is expected between different formations, but is not well established (except for the
Zechstein formation). Both initial value and stress development as a consequence of depletion should
be measured when having the opportunity (drilling of new wells, workovers).
Reservoir pore pressure in the Rotliegend reservoir is well established from production and observation
wells, but there are areas in the field with poor well coverage. The main area of uncertainty is the
pressure field over time in the underlying aquifer and in the overlying Ten Boer Claystone. Data
acquisition is needed to reduce these uncertainties.
Pore pressure evolution within faults is not known and cannot be measured in the subsurface. However,
the impact of this process can be evaluated through modeling. This is the objective of the ongoing
vibration analysis study (2D evaluations).
The accuracy of determining hypocenter locations needs to be improved to differentiate between
possible failure mechanisms. Both the installation of down hole geophones and additional shallow
geophones fulfill this objective.
Time-delayed compaction and other irreversible deformations that could delay or reduce fault slippage
is the subject of ongoing research (KNAW, 2013).
Advanced 3D geomechanical modeling is carried out to investigate the stress evolution in and around
the producing Groningen field and its relation to the reactivation of faults. The models also serve to
evaluate the effects of different production scenarios in the framework of TBO research topic 6.

More options for additional data gathering are being reviewed. These include:



Post failure behavior and unloading (cyclic) behavior of reservoir and cap rock could be investigated
by laboratory experiments. Also, monitoring and associated modeling studies in other fields could be
considered to gain a better understanding of the formation behavior.



Onset of fault slip and subsequent post-failure behavior can also be studied through fundamental lab
experiments.

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9.8

References

Brudy, M., Kjørholt, H. (2001) Stress orientation on the Norwegian continental shelf derived from borehole
failures observed in high-resolution borehole imaging logs. Tectonophysics 337, pp. 65-84.
Davison, J.M., Ita, J., Hofman, R.A., Seli, P. and Ong, P., (2010), Ensuring Production in the Malaysian F6
Field Using Field Monitoring and Geomechanical Forecasting, SPE 132195, Int. Oil & Gas Conf. and
Exhibition, 8-10 June 2010, Beijing, China.
Glab, M. and Van Eijs. R (2001), Localisation of Sensitive Reservoir Settings for Induced Seismicity using
FEM, Report TNO-NITG 00-320-A
Hettema, M.H.H., Schutjens, P.M.T.M., Verboom B.J.M., Gussinklo, H.J., (2000) Production-Induced
Compaction of a Sandstone Reservoir: The Strong Influence of Stress Path. SPE Reservoir Eval. & Eng. 3 (4)
PP. 342-347.
Kanamori, H. (1994), Mechanics of Earthquakes. Annual Rev. Earth Planet Sci., 22, p207-237.
KNAW (2013) Resolution inauguration the scientific steering committee for the long-term subsidence study
in the Wadden Sea region with a brief description of its tasks.
Mulders, F.M.M. (2003), Modeling of stress development and fault slip in and around producing gas
reservoirs, Ph.D. thesis, Delft University of Technology.
Orlic, B. and Wassing, B.B.T. (2012), Modeling stress development and fault slip in producing
hydrocarbon reservoirs overlain by rock salt caprocks, ARMA 12-145, 46th US Rock Mechanics /
Geomechanics Symposium, Chicago, USA, 24-27 June 2012.
Roest, J.P.A., Kuilman, W. (1993). Geomechanische Analyse van de lichte aardschokken in het Eleveld
reservoir, TU-Delft.
Segall, P. (1989). Earthquakes triggered by fluid extraction. Geology, Vol. 17, pp. 942-946.
TNO-DIANA bv, DIANA: Finite-Element Program and User Documentation, version 9.4.3, 2011
Van den Bogert, P. Van Eijs, R., van der Wal, O (2013) Groningen Fault Stability Assessment
Van Eijs R., van Dalfsen, W. (2004) Borehole observations of maximum horizontal stress orientations in the
Dutch upper crust. TNO-NITG – Information, December 2004.
Van Eijs, R.M.H.E., Mulders, F.M.M., Nepveu, M., Kenter, C.J., Scheffers, B.C. (2006) Correlation between
hydrocarbon reservoir properties and induced seismicity in the Netherlands. Eng. Geology, 84, p99-111.
Van Thienen-Visser, K., Nepveu, M., Hettelaar, J. (2012) Deterministische hazard analyse voor
geinduceerde seismiciteit in Nederland (in Dutch). TNO report R10198.
Visser, C. (edt.), Groningen Field Review 2012, Static Modelling and Hydrocarbon Volume Determination,
Document number: EP201203204663

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10 Reducing the Remaining Uncertainty in the Hazard
10.1

Introduction

This report and the previous report issued to the TBO [Ref. 1], describe the results of the study program into
induced earthquakes in the Groningen field, coordinated by NAM and carried out in the second half of
2012 and 2013. A Study and Data Acquisition Plan was issued October 2012 and has since then been
regularly updated during 2013 [Ref. 2 and 3]. This study work carried out over 2012 and 2013 forms the
technical basis for the seismic hazard analysis of the Groningen Winningsplan 2013. Despite the progress
made, some technical questions remain unresolved, while uncertainties in the geomechanical parameters
and in the estimated seismic hazard are large.
Some of the remaining uncertainty stems from lack of knowledge (epistemic uncertainty) and is therefore
liable to be further constrained with increasing data acquisition and analysis.

10.2

Data acquisition activities

10.2.1 Introduction
Objectives of the research on induced seismicity in the Groningen Field are to increase our understanding
of the mechanism inducing the earthquakes and the resulting hazard of damage (e.g. to buildings) and if
possible to define measures to minimize this hazard.
High resolution mapping of seismicity and lowering the current detection and location magnitude threshold
is expected to be essential in a better geomechanical understanding of the earthquake hazard. The
improved determination of the locations of the hypocenters of the earthquakes will allow better tie-in with
the structural model of the Groningen Field.
To improve the resolution over the whole Groningen field an extension of the (existing) passive seismic
monitoring network will be implemented with installation of additional seismometers and accelerometers
(Figure 10.1. Installation of two subsurface installed vertical arrays in the Loppersum area will improve the
determination of earthquake hypocenters.
10.2.2 Extension passive seismic network
The current configuration of the Groningen monitoring network has provided field-wide coverage since
1995 for the detection and location of all events with a magnitude bigger of equal to 1.5 (M>=1.5 events).
The network consist of 14 borehole stations, 8 installed in 1995 and extended in 2010 with an additional
6 stations, plus 12 accelerometers.
This network recorded a catalogue of 188 M>=1.5 events between 1995 and 2012 and is the primary
basis for current earthquake hazard assessment within the field. The horizontal location uncertainties are
typically about 0.5 km and the events are assumed to be at a depth of 3km.
Extension of the monitoring network would have two key advantages over the existing network, more
sensitivity and more accuracy.
More sensitive as more reliable detection and locate more of the M<1.5 events within the field to allow
more robust statistical analysis of the relationship between the number of earthquakes and gas production.
More accurate as measure earthquake locations with sufficient accuracy to reveal their relationship with
mapped faults and their depth distribution relative to the reservoir to understand what causes these
earthquakes.
To deliver the earthquake data necessary to realize these objectives, the performance criteria for the
network are considered to be:



Detect 10 events for every M>=1.5 event. Given the existing earthquake population has a b-value of 1,
this means reducing the magnitude of completeness from the current M=1.5 to M=0.5.

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

Locate all detected events with a standard horizontal error of less than 200 m and a standard vertical
error of less than 500 m.

To improve resolution the network will be extended with 60 boreholes, spread in 6x6km grid covering the
whole Groningen Field (Figure 9.12). Each 200m deep borehole will be equipped with 4 levels of
geophones, one accelerometer at surface including required electronics for data-transmission, preamplifiers and communication means.

Figure 10.1

Extension of the shallow borehole geophone network.

Additionally 4 boreholes equipped with strong motion sensors (low frequency geophones) will be realized
in order to faithfully record the signals for the very largest events without clipping and enable ongoing
recalibration/updating of the ground motion attenuation relations measure high range earthquakes.
10.2.3 Subsurface vertical seismic arrays
Down hole seismic monitoring allows monitoring at substantially reduced noise conditions thereby
improving signal-to-noise ratio, magnitude detection threshold and precision of the hypocenter
determination. From the surface installed seismic network the vertical location of earthquakes are assumed
to be at a depth of 3km. Implementation of a subsurface seismic array would lead to accuracy in horizontal
and vertical location of events.
Challenges in borehole seismology are a secure deployment of sensors, safe date transmission to surface,
formation temperature and pressure and reservoir fluids. Thereby drilling is expensive, risky and logistically
difficult and requires preparation time. As down hole instruments need to operate at elevated pressure and
temperature conditions proper engineering is essential.
Objective is to have two subsurface installed vertical arrays in the Loppersum area with sensors covering
Zechstein down to Carbonferious. The sensors should be suited to a target event magnitude range of M=-

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2.5 to M=+1 over the distance range 500m to 10 km, and a system minimum sampling frequency 2KHz
(0.5ms data) or higher.
10.2.4 Temporary vertical seismic arrays
With the objective to implement subsurface measurements as soon as feasible two existing observations
wells in the Loppersum area (Stedum-1 and Zeerijp-1) where selected to be modified into seismic
observation wells. As these wells have been drilled more than 35 years ago, and not initially designed as
seismic observations wells it was proposed to temporary install vertical seismic arrays without performing a
workover. Advantage of this solution would be deployment on short notice, disadvantage vertical coverage
is limited to the reservoir section only.
Based on the experience gained over the last 3 years in Bergermeer (Taqa) a system has been designed
which will be installed in October 2013. Two arrays with respectively 8 and 11 levels, with 30m station
spacing will be installed. The installation requires well killing, installation of additional pressure control
equipment, and communication and data collection equipment. Objective is to have the equipments
operational until the permanent monitoring wells are in place (about a year). Continuous monitoring will
start immediately after installation; and data will be made available via GSM connection to KNMI and
Magnitude for further analyze.
10.2.5 Permanent vertical seismic arrays
For the permanent vertical seismic arrays it has been advised to realize newly drilled dedicated observation
wells. In a Request for Information contractors have been invited by NAM to propose their solution for
permanent seismic monitoring systems. It is the objective to have an operational permanent seismic
recording system in the fourth quarter of 2014.

10.3

The 2014 Study and Data Acquisition program on Induced earthquakes in the Groningen
field

During 2014 NAM will continue the research program to gain further insights in the processes leading to
earthquakes and the size of the resulting hazard. The analysis of the contributions to the epistemic
uncertainty in the hazard using the logic tree approach, showed four factors to be important: the strain
partitioning factor describing the coupling between compaction strain and seismicity, the ground motion
prediction equation, the compaction and to a lesser extent the b-value.

Figure 10.1

Variation in the maximum PGA found within the Groningen Field with a 2% chance of exceedance over
the period 2013 to 2023, due to epistemic uncertainties represented in the logic tree.

The partitioning factor has the largest impact on the uncertainty in the seismic hazard. Insights and limits to
the partitioning factor will most likely have to come from outside the current hazard framework. Both
attempts to gain insights from analogy with tectonic earthquakes and further geomechanical investigations
are elements of the study program to improve the understanding of process inducing the earthquakes and
the partitioning factor. Table 10.1 shows the main activities in the research program into induced
earthquakes in the Groningen field for 2014.

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10.3.1 Geomechanical discussion to reduce uncertainty in the partitioning factor
A seismologic model developed to characterize future seismicity (ref Report to the Technical Guidance
Committee (TBO) on Subsurface Aspects of Induced Earthquakes in the Groningen Field) is based on the
concept of induced earthquakes accommodating depletion-induced strains. A relationship developed
between historical seismicity and reservoir compaction is used as a framework for assessing future
seismicity based on future compaction induced by planned gas production. This relationship (partitioning
factor) has previously been inferred by comparing total seismic moment (from measured data) with
calculated values of compaction (from geomechanical/compaction models which are well constrained by
measured subsidence data). Significant variability has been observed in these comparisons.
To better understand this variability, it should be considered that the relationship between compaction and
seismicity should depend on the characteristics of faults in the vicinity of the compacting region(s).
Geomechanical/compaction models used in current seismological analysis have treated faults only in an
implicit, statistical manner. At present there is no field evidence that seismicity in the Groningen field is
governed by regions with different fault characteristics (e.g., presence or absence of faults, fault geometry
and orientation, stress exerted on faults, etc.) and as such, the model does not distinguish between regions
that may have similar compaction characteristics, but different fault characteristics. Because the true
distributions of fault characteristics are unknown, multiple factors are being “lumped together”, resulting in
variability that may be greater than the true physical uncertainty in partitioning factors.
The geomechanical / fault movement FEA models described in section 9.6.1 and section 9.6.2 may
provide the capability to reduce the uncertainty in partitioning factor calculations, even prior to collection of
additional field data. A number of these models have shown good agreement with field measurements and
because they explicitly account for faults (a subset of faults interpreted from seismic data was included in
the model), this is a promising approach to provide a physically meaningful basis for relating compaction
strain energy with energy exerted during fault slip (labeled dissipated energy in prior Figures).
Results shown on Figure 10.2 illustrates the potential advantages of this approach:
• Temporal evolution of fault energy dissipation from geomechanical models correlates reasonably
well with observed radiated energy from seismic events
• As a first step the partitioning or scaling factor that relates radiated energy with dissipated energy
was assumed to stay constant within the analyzed period (1996-2012).
• Predicted energy dissipation is significantly greater in SM1 than SM2 regions; in agreement with
observed seismicity

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Figure 10.2

Total observed (square symbols) and modeled (blue line) radiated energy since 1996 as a function of
gas production for sub-model regions 1 and 2. The modeled radiated energy is determined from the
fault dissipated energy (calculated from the FEA models) by applying a single valued, constant
partitioning factor. Observed radiated energy is calculated from M≥1.5 events.

While the comparisons in Figure 10.2 are encouraging, further improvement may be established by
enhancing the representation in the FEA models of the faults, fault behaviors (e.g., slip / velocity
weakening), compaction physics (e.g., non-linear, rate-dependent compaction), etc. Also the full range of
models consistent with the historic seismicity data accounting for the stochastic variability due to finite
sample effects within the earthquake catalogue should be assessed.
By providing a “physics-based” constraint on the relationship between compaction strain energy and fault
dissipated energy, these models could reduce the variability in partitioning factors that had previously been
calculated based on models without explicit consideration of faults.

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10.3.2 Continuation of the Study Program
Main Uncertainty
Compaction

Study and Data Collection to reduce Uncertainty






Partitioning Factor &
Improved Geomechanical
understanding of the
rupture process

Update of the porosity model for the Groningen field in view of new
measured subsidence data.
Geodetic analysis of the measured subsidence data, using SURE (software
package to derive subsidence from all available geodetic measurements in
an integrated manner over time and space) to obtain a best estimate of the
full subsidence field.
Improved matching of the models with measurements, based on a formal
minimum acceptance criteria test to better assess the validity of the
different compaction models.



Rock measurements on core samples and theoretical investigations to
improve models of the time-dependent compaction behavior.



Review with group of external experts to look for possible avenues of
research, which may enable us to constrain the value of the partitioning
factor at the upper end. These may include a literature search for the
partitioning factors and the fault aspect ratios seen elsewhere.



The pre- and post-failure behaviour of faults and adjacent formations
determines the energy storage and release prior to and during seismic
events. The following activities are foreseen:









Experimental testing into the dynamic fault friction behavior, failure
and unloading characteristics of reservoir rock, to better understand
the physics of fault re-activation and rupture and the conditions
which determine whether fault slip is seismic or a-seismic. Also the
effect of pore fluid needs to be investigated.
Modeling studies to evaluate experimental results and the impact of
various geomechanical modeling options on the physics of fault reactivation and rupture (geometrical aspects (reservoir thickness, fault
dip angle and dip azimuth angle), fault slip softening and stiffness
and stress contrast).

Detailed geomechanical modeling to investigate whether a rupture
initiated in of below the Rotliegend reservoir section will propagate into
the Zechstein salt or whether the Zechstein salt will limit rupture growth in
the case of Groningen.
Currently, the association of the earthquakes with identified faults in the
structural model is hampered by the large uncertainty in the hypo-centre
location of the earthquakes and uncertainty in the characterization of the
structural model.
Two initiatives have been initiated to address this issue by improving
hypocentre location of the earthquake and characterization of the source
radiation parameters:



Extension of the field wide geophone network consisting of some 60
borehole stations each with a string of geophones with 30 meter
spacing and a surface accelerometer. In addition, KNMI will install a
further 4 broadband sensors.



Placement of geophones at reservoir level in deep wells located in
the Loppersum area. Over the limited region covered by these deep

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arrays the accuracy of measured event depths will be greatly
enhanced.







Accurate imaging of the earthquake hypocenter requires knowledge of the
3D velocity field. Measurement of the rock density and P- and S- wave
velocities in the overburden well sections of Borgsweer-5 well and future
wells aim to improve our knowledge of the 3D velocity field (see also
GMPE).
Advanced multi-scale mechanical modeling combined with a
geomechanics-seismicity relationship might provide a suitable framework
to better understand seismic fault slip behavior and assess future seismicity
via a physically-constrained strain partitioning (i.e., partition factor). This
will be the subject of future study work, which will aim to establish a
workflow leading to a robust integration between 3D explicit fault
modeling and the existing Seismological Modeling to reduce seismic
uncertainty.
Improvement of the current full field geo-mechanical modeling, to
investigate the possibility of sub-surface stress management. Results of the
aforementioned model selection studies are to be incorporated. Further
calibration is supported by additional field measurements of:



Ground Motion
Prediction Equation








b-value



The minimum horizontal stress across the field, in particular in and
around the depleting formations.
The pore pressure field over time in the underlying aquifer and the
overlying Ten Boer claystone.

Surface ground motion data for further calibration of the GMPE on a much
denser grid than currently possible will be obtained by the accelerometers
placed in the cellars of some 60 geophone wells of the extension of the
network. Data from the geophones in the 60 shallow wells will also be
valuable for characterizing site response.
Conduct site response analyses (based on field measurements of Vs30 and
borehole geophone data) to give spatially varying site corrections and a
field-specific rather than generic site amplification model.
Waveform modeling and full waveform inversion (FWI) to understand the
impact of spatial heterogeneity of rock properties and the role of the
Zechstein interval in attenuating amplitudes from source to surface and
also to give insight into possible alternative forms of GMPE.
To improve the modeling of propagation of the seismic wave field through
the subsurface, the velocity field in the rock overlying the reservoir section
needs to be known in greater detail. If well design allows, measurement of
the rock density and P- and S- wave velocities in the overburden will be
acquired in future wells.
The densified seismic array over the Groningen field will enable the
earthquake catalogue to be extended by detection of larger numbers of
smaller events (M0.5) distributed across the field. The overall b-value for
the catalogue is already well constrained and we have found that
variations in b have a limited impact on the predicted hazard.
Nevertheless, the new data from the densified array should improve our
ability to calculate b-values for subsets of the data catalogue and so better
identify variations in b with location and compaction.

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Table 10.1

Main components of the 2014 Study and Data Acquisition program on Induced earthquakes in the
Groningen field.

Apart from study and data acquisition work to better understand and assess the seismic hazard, NAM will
also continue investigating options into measures to reduce seismicity. Table 10.2 lists these studies.
Measures

Study Identify and Assess the effectiveness of Measures

Pressure
Maintenance



The study on pressure maintenance options for Groningen field provides an
outline for an nitrogen injection development of the Groningen field. Feasibility
of this has yet to be demonstrated and implementation of such a development
has a relatively long timeline.
A follow-up feasibility study into pressure maintenance is planned to be started
early 2014. This will address the main technical feasibility of the sub-surface
gas recovery process and the required facility infrastructure aspects of such a
pressure maintenance development. The assessment of the large impact of
such major development on the Groningen community, integration and
identification of potential synergies with other industrial activities will also be
part of this study.

Stress
Management



Using sub-models of the field, geomechanical studies on fault stability will be
continued. Based on this study effort the potential for active stress management
of the reservoir will be investigated.



Table 10.2

10.4

Impact of pressure management of the blocks in the western periphery of the
field will be assessed using a full-field or sector geomechanical model
Optimisation of off-take from the clusters based on reservoir stress
management.

Main components of the 2014 Study and Data Acquisition program on identification and assessment of
effectiveness of measures to reduce the impact of seismicity.

References

[1]

Report to the Technical Guidance Committee (TBO) on Subsurface Aspects of Induced Earthquakes in
the Groningen Field, October 2013, NAM.

[2]

Study and Data Acquisition Plan for Induced Seismicity in Groningen Planning Report, November
2012, Jan van Elk and Dirk Doornhof

[3]

Study and Data Acquisition Plan for Induced Seismicity in Groningen Planning Report, August 2013,
Jan van Elk and Dirk Doornhof

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11 Appendix
A:
methodology
11.1

Full

3D

geomechanical

modeling

Introduction

Full 3D geomechanical modeling approach integrates geologic, geomechanics, and production data to
simulate field deformation from the start of production. Due to the large scale of the field, and significant
number of faults, a multi-scale approach that combines large scale model to characterize field wide
behavior, and finer scale sub-models to understand production-induced fault slippage is used. A global
model of the entire field, without faults modeled explicitly, is developed to characterize the stress states in
the entire regions and model the surface subsidence. Two sub-models, with selected faults included, for
smaller regions in areas of observed seismic activity were developed to study production induced fault
slippage.
The full 3D geomechanical modeling methodology is based on the load-resistance approach to fault
reactivation, wherein reservoir pressure changes due to depletion lead to deviation of the stress state from
initial conditions (load) and that may consequently lead to fault slippage governed by the frictional strength
of the fault (resistance). This methodology is summarized in Figure 11.1.
Initial Condition
Load
Resistance
Initial Stress State

+

Production  Pressure Change

Fault Strength

+

+


Extension

tan    

Production-induced
slip

(friction)

Reservoir
compaction
Reservoir compaction  fault slip?

Production-induced
stress changes

H

V



Initial stress
Fault stress activation

Figure 11.1

Load-resistance (production induced stress changes and frictional strength of faults respectively) based
approach to fault slippage modeling

The global model will be used to study effect of reservoir depletion on stress changes on the entire field
scale, and smaller scale sub-models with detailed fault modeling will be used to predict fault slippage and
the frictional dissipated energy. Various production scenarios may be ranked based on these predictions.
The details of the geological, material and stress input data used to initialize the model, and brief
description of the global and sub-modeling methodology are included in the following subsections.

11.2

Geological model input

The finite element model honors NAM-derived GFR2012 static modeling framework from Petrel for
reservoir zonation. The model contains 22 structural horizons from ground surface to base of model, and
contains 12 reservoir model units from base of Zechstein to base of lower Slochteren.

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Figure 11.2

Geological model structural horizons and fault framework

Figure 11.3

Geological layers included in finite element model

The global finite element model includes 21 layers as indicated in Figure 11.3. Names of the horizons and
layers in geologic model and the unit numbers used in NAM reservoir simulator analyses are included in
the figure. The global model includes all these layers up to depth of 20km (the Carboniferous layer is
extended to reduce effect of boundary conditions). The sub-models include all layers from salt layer ZE_Z2
to Carboniferous up to depth of 4km.

11.3

Material properties

The material properties for reservoir layers are based on test data provided by NAM. Properties for all
other layers except Anhydrite are provided by NAM. The Anhydrite properties are based on data from
literature since no field specific tests or data are available. The elastic properties, densities and the source
of the data are summarized on Table 11.1.

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Table 11.1

Elastic material properties and densities for soil layers

Unit

Young’s Modulus
Poisson's Ratio Density (kg/m 3) Data Source
(GPa)

North Sea

2

0.3

2150

NAM

Chalk

10

0.25

2350

NAM

Rijnland

16

0.25

2350

NAM

Triassic

16

0.25

2350

NAM

Halite

30

0.35

2100

NAM

Andydrite

70

0.25

2900

Literature

40

0.2

2300

NAM

Heterolithic

36

0.2

2300

NAM

Reservoir

Porosity-Dependent

Zechstein
Ten Boer
Slochteren

Carboniferous

40

Core Data
0.2

2300

NAM

Young’s Modulus (GPa)

The Young’s modulus, Poisson’s ratio, density and the Biot coefficient of the reservoir layer are dependent
on porosity. This dependence is captured in the finite element model by defining porosity as a field variable
and these properties to be a function of this field variable. The plots of these properties vs. porosity and the
fit used in the model are shown in Figure 11.4 through Figure 11.7. The data for Young’s modulus,
Poisson’s ratio and Biot coefficient from different well locations is indicated by different colored symbols as
indicated by plot legends.

Porosity
Figure 11.4

Young’s modulus vs. porosity of reservoir rock

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Poisson’s Ratio

Confidential

Porosity
Poisson’s ratio vs. porosity of the reservoir rock

Density (g/cm3)

Figure 11.5

Log Data
Fitting Line

Porosity
Density vs. porosity of the reservoir rock

Biot Coefficient, 

Figure 11.6

Porosity
Figure 11.7

Biot coefficient vs. porosity for the reservoir rock

The plasticity properties for reservoir layers are based on core sample triaxial test data provided by NAM.
The data fit and parameters for Drucker-Prager material model are shown in Figure 11.8.

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q (MPa)

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Pressure, p (MPa)
Figure 11.8

Peak shear failure envelope of reservoir rock and fitted Drucker-Prager model parameters

The faults are modeled using Coulomb friction model with the friction coefficient based on the residual
strength of reservoir rock. The plot of residudal strength (shear stress) vs. normal stress in triaxial tests in
included in Figure 11.9. The slope of the fitting line indicates an upper bound for coefficient of friction for
the faults; however, the actual strength of the fault is likely to be lower. Sensitivity studies using the submodels indicated that a friction coefficient of 0.48 provides a good match for predicted slip magnitude and
dissipated energies with field data for production history (1964-2012).

=0.65
=0.40
Test Data

Figure 11.9

11.4

Residual strength of the reservoir rock

Initial stress state

The initial stresses in the soil layers are defined using the following gradients provided by NAM:




Vertical stress: 2.14 bar/10m
Maximum horizontal stress: 1.71 bar/10m
Minimum horizontal stress: 1.60 bar/10m

The maximum horizontal stress azimuth is 160o from North. The stresses in the two halite layers are
isotropic, i.e. both the horizontal stresses are equal to the vertical stresses. The horizontal stresses in
anhydrite layers are also close to the vertical stress; however, the stress state need not be perfectly isotropic.
A separate “salt-creep” step is run prior to production pressure history/forecast analysis to ensure isotropic
stress state in halite layers, and higher stresses in anhydrite layers.
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The reservoir pore pressure values in finite element model are interpolated and mapped from the pressure
values from NAM reservoir simulator analyses, and are applied as initial and boundary conditions for all
pore pressure degrees of freedom in the finite element model.

11.5

Global model analysis methodology

The global model includes the entire Groningen field region, with additional sideburden added on all sides
to reduce the effect of boundary conditions. This model does not explicitly include faults. The Groningen
field region in the global model is 42.8km x 50.6km. Overall horizontal dimensions of the model are
182.8 km x 190.5 km x 20km depth. The depth of 20km, much larger than reservoir layer depth of
approximately 3km, is similarly used to reduce the effect of the bottom boundary. A vertical cross-section of
the global model mesh is shown in Figure 11.10. The average thickness of various soil layers, number of
element layers within soil layers and average element thicknesses are summarized in Table 11.2. The mesh
includes approximately 800,000 elements.

6km
70km

14km

50.6km

70km

Global Model Vertical Cross Section

Figure 11.10 Groningen field region with modeled rock layers (top figure); vertical dimensions scaled by factor of 10;
sideburden not included. Global model vertical cross-sections (bottom figure) showing Groningen field
region and entire model with sideburden.

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Layer
Elems across layer Avg elem thk Avg layer thk
CARBONIFEROUS
8
343.40
2747.20
LSS_1_RES
3
10.80
32.40
LSS_1_HET
2
2.07
4.14
LSS_2_RES
4
10.34
41.34
LSS_2_HET
2
2.84
5.68
USS_1_RES
3
10.65
31.94
USS_1_HET
4
1.59
6.37
USS_2_RES
3
10.59
31.77
USS_2_HET
2
2.08
4.17
USS_3_RES
2
10.23
20.46
TEN_BOER_Z1_2
1
25.62
25.62
TEN_BOER_Z3
1
14.23
14.23
TEN_BOER_Z4
1
12.57
12.57
ZE_BASAL
2
31.42
62.83
ZE_Z2
6
78.64
471.83
ZE_Z3
2
29.14
58.28
ZE_Z4
5
44.30
221.49
TRIASSIC
6
77.65
465.91
RIJNLAND
1
82.43
82.43
CHALK
3
263.95
791.85
NORTH_SEA
4
216.87
867.48
Table 11.2

max TVD
6000.00
4242.21
4195.93
4193.93
4157.85
4155.85
4152.85
4127.49
4092.10
4090.10
4056.85
4026.36
4003.46
4000.57
3693.78
3531.04
3529.04
3442.23
2317.98
2219.47
1431.42

min TVD
2721.17
2718.17
2716.17
2703.30
2700.68
2678.66
2672.40
2632.82
2629.49
2612.21
2595.23
2582.21
2569.98
2510.62
1100.85
986.00
436.68
378.85
376.89
190.83
0.00

Soil layer mesh details

Sum (avg layer thickness) =
5999.999991
The global model analysis includes three steps: (i) initial geostatic stress initialization, (ii) salt-creep step to
ensure isotropic stress state in halite layers and higher horizontal stresses in anhydrite layers, and (iii)
production pressure history/forecast analysis. The effect of salt creep was also included during the
production analyses. The analysis takes approximately 50 hours to run on 256 cpus.

The first two steps above, geostatic initialization and salt-creep are required to ensure correct initial stress
state, as described in above subsection, before start of production pressure history/forecast analysis. This
stress initialization was verified by plotting stresses along 100 vertical lines in a 10x10 grid pattern over
the Groningen field region, and at 70 locations within the sub-model- 1 region as indicated in Figure
11.11.

Figure 11.11 Locations for stress vs. depth plots for verification of initial stress state

The initial stresses along these vertical lines are plotted in Figure 11.12 through Figure 11.15. Note that
the stresses are approximately aligned around the specified gradient for all soil layers except halite and
anhydrite. There are deviations from the specified gradient due to the effect of salt-creep (isotropic stress
state in salt also modifies stresses in surrounding region). Additionally, there are some deviations due to
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numerical limitations of the model. However, note that these numerical errors are mainly present for
regions near the boundary of the Groningen field region. The stress plots for the sub-model-1 region are
significantly smoother.

Figure 11.12 Global model sHmax at the 100 locations after salt-creep step

Figure 11.13 Global model sHmin at the 100 locations after salt-creep step

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Figure 11.14 sHmax stress in the sub-model-1 region (output from global model analysis)

Figure 11.15 sHmin stress in the sub-model-1 region (output from global model analysis)

The production history analysis for years 1964-2012 based on geological model realization G1 with
moderate strong northern aquifer was used to predict surface subsidence and reservoir strains. The
contour plot of predicted surface subsidence in 2012 and comparison with field data is shown in
(a)
(b)

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Observed seismic events

Error in subsidence predictions
Size of circles indicates
magnitude of error
Blue-green colors: prediction
lower than field data
Orange-red: predictions higher
Note that errors are towards
edges of sub-model regions;
away from observed seismic
event locations

(c)

(d)

Figure 11.16. (a) and (b). A map of error between model predictions and field data is included in (d),
which indicates that errors in subsidence predictions are very low in areas of significant seismicity shown in
(c).
Subsidence Predicted by Model (cm)

35
30
25
20

15
10
5

0
0

10

20

30

Field Subsidence (cm)

(a)

Observed seismic events

(b)

Error in subsidence predictions
Size of circles indicates
magnitude of error
Blue-green colors: prediction
lower than field data
Orange-red: predictions higher
Note that errors are towards
edges of sub-model regions;
away from observed seismic
event locations

(c)

(d)

Figure 11.16 (a) Contour plot of predicted surface subsidence (m) in 2012, (b) comparison with field data, (c)
locations of seismic events from NAM, and (d) map of error between model subsidence predictions and
field data

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The predicted average strain in the reservoir layers is compared with available field data. The comparison
for one well location, De Hond, is shown in Figure 11.17. The average strain is computed over reservoir
(reservoir rock and heterolithic layers) only and over reservoir and ten boer layers combined, since the
averaging interval for the field data is not known.

1980
0.01%

1990

2000

2010

0.00%
, zero in 1983

Strain (%)

-0.01%
-0.02%

Normated on DC,
zero in 1983

-0.03%

FE average over
TB & RES

-0.04%

FE average over
RES

-0.05%

-0.06%
Figure 11.17 Comparison of predicted reservoir layer strains vs. field data (De Hond well location)

11.6

Sub-model analysis methodology

Two sub-models have been developed to model the regions of seismic activity in greater detail, including
detailed modeling of faults as contact surfaces that permit slippage. The sub-models include soil layers
from ZE_Z2 halite to the Carboniferous up to depth of 4 km. The dimension of sub-model-1 and -2 are
16.3km x 20.0km and 12.7km x 19.2km respectively.
More than 1700 faults have been interpreted in the Groningen field, of which 707 have been used to
construct the static and dynamic reservoir model. Due to extreme complexity of meshing with faults, a
selected number of faults were included in the sub-models. The fault selection was based on: (i) faults
along major/compartment boundary, (ii) fault throw, (iii) shear capacity utilization from NAM analysis,
and (iv) the proximity to seismic events. The first step in fault selection process is to include the faults along
major/compartment boundaries. Additional faults according to each additional criteria area added in
subsequent steps. This process for sub-model-1, with faults added in each step, is shown in Figure 11.19.
Sub-model-1 includes 40 faults and sub-model-2 includes 37 faults. Both meshes include approximately
250,000 elements, with over 99.7% hexahedral (brick) elements to ensure accuracy of the solution. The
three-step analysis (same as global model) takes approximately 30 hours to run on 128 cpus. The locations
of these two sub-models relative to the Groningen field region in the global model are shown in Figure
11.18.

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Figure 11.18 Location of sub-models; sub-model-1 indicated by black lines, sub-model-2 indicated by magenta lines
Location Sub-Model #1

Faults with Large Throws

Boundary-Major Faults

Boundaries of Compartments

SCU Critical Faults

Faults Close to Seismic Events
40 Faults

Figure 11.19. Fault selection for sub-model-1; total of 40 faults included after final step

The meshes for the two submodels, with faults highlighted, are shown in Figure 11.20.

Figure 11.20 Sub-model-1 and 2 meshes with faults highlighted

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The sub-model analyses are run separately from the global model, with the exterior node boundary
conditions driven by displacements in the corresponding global model analysis. Figure 11.21 through
Figure 11.24 show the comparison of stresses, displacements and pore pressures in the top reservoir layer
of the sub-models with those in global model to verify consistency of sub-models with the global model.
S11 (East – West)

S22 (North – South)

Figure 11.21 Initial stresses (in MPa) in sub-model-1 top of reservoir layer USS_2_Res compared with the global
model stress field

S11 (East – West)

S22 (North – South)

Figure 11.22 Initial stresses (in MPa) in sub-model-2 top of reservoir layer USS_2_Res compared with the global
model stress field

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Submodel 1

Submodel 2

Figure 11.23 Horizontal displacement (East-West, in meters) for top of layer USS_2_Res in sub-model-1 and -2 in
year 2012 compared with global model; comparisons for other two displacement components indicate
similar match
Submodel 1

Submodel 2

Figure 11.24 Comparison for 2012 pore pressure (in MPa) for top of reservoir layer USS_2_Res

The sub-model analyses are used to predict production induced fault slippage and the energy dissipated
due to this slippage. Fault slip is predicted at different locations and at different points in time. The contour
plot of slip magnitudes along faults for sub-model-1 analysis for base case development scenario (D1)
based on Geological model realization G1 is shown in Figure 11.25. The predictions for dissipated
energies for various cases are compared in the main text of this report.

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Vertical path along fault M1,
used below

Figure 11.25 Contour plot of slip magnitudes in 2059 along faults of sub-model-1 for the base case production
scenario

Evolution of slip along the vertical path shown in Figure 11.25 with time is shown in Figure 11.26. This
path is located at the region with maximum slip magnitude along fault M1.
0.04

2059

0.035

2049

0.03

2034

Slip (m)

0.025

2024
2018

0.02

2012
0.015

2004

0.01

1984

0.005
0
2800

-0.005

2900

3000

3100

3200

3300

3400

Depth (m)

Figure 11.26 Evolution of slip magnitude along vertical path shown in Fig. A1.24 for the base case production
scenario

The slip magnitudes and the extent of the vertical section of the fault that is slipping both increase with time.
However, note that the slip is zero near top and bottom in anhydrite and carboniferous layers respectively.
This indicates that the slip magnitudes have not “saturated” due to zero-slip boundary conditions at top
and bottom. Additionally, as shown in Figure 11.26, the slip magnitudes do not increase significantly
beyond approximately year 2050, since there is significantly less depletion beyond this year compared to
earlier years.
The predicted dissipated energy is very sensitive to the friction coefficient used to model faults. Predictions
for evolution of dissipated energy for various friction coefficients for sub-model 1 are compared in Figure
11.27. This sensitivity study was used to identify the friction coefficient value of 0.48 for use in the submodels. This value was used consistently to model all production scenarios so that the predicted energies
for different scenarios can be compared directly.

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Figure 11.27 Dissipated energy for different coefficients of friction for production history (1964-2012) analysis;
geological model realization G1; sub-model 1

The sub-models include a limited number of faults that are selected according the prioritization criteria
described above. This prioritization process ensures that faults that are most critical to seismic activity are
included. Results from sub-models predict that nearly 90% of dissipated energy is due to only 10% of the
faults that are included. A plot of the contribution to dissipated energy by individual faults is included in
Figure 11.28. This comparison indicates that the assumption that critical faults are captured by the
prioritization process is reasonable. Adding more faults, either those that are currently excluded or others
that have not been imaged by seismic surveys, are not expected to change the predicted dissipated energy
significantly.

Figure 11.28 Contribution to dissipated energy by individual faults; 10% faults contribute 90% of energy

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12 Appendix B: Pressure Maintenance
12.1

Introduction

In a letter to parliament (11 February 2013 reference TK 2012-2013 33529, no. 2), the Minister of
Economic Affairs initiated a number of studies on induced earthquakes in the Groningen area. The Minister
also installed and confirmed the TBO (Technische Begeleidingscommissie Ondergrond; Technical Guidance
Commission Subsurface), to oversee the study work done by NAM and to provide independent assurance.
One of the studies (number 6) relates to the subsurface operation of the field and addresses the question:
“Are there alternative gas recovery methods that result in a reduction of the seismic activity”?
Study number 5 has been addressed in the; “Report to the Technical Guidance Committee (TBO) on
Subsurface Aspects of Induced Earthquakes in the Groningen Field”, which has been issued previously. It
describes the technical studies carried out by NAM and reports the gained insights to the TBO.
Study number 6 is a continuation of this work. It consists of two parts. Part 1 addresses the impact of
different depletion scenarios on seismicity, and has been issued separately. The current document reports
on part 2, which addresses development options for the Groningen field based on pressure maintenance
by injection of fluids.
Part 2 consists of 5 sections, including this introductory section. The other sections are briefly introduced
below.
2. This section describes the selection of the injection medium for pressure maintenance. Several fluids
are considered, but nitrogen is selected as the most suitable option.
3. This section describes the modelling of the displacement and mixing of the injected nitrogen and the
indigenous natural gas. Production forecasts and reserves estimates are prepared for a basecase
development concept. Several alternative development scenarios are evaluated in parallel.
4. This section describes the surface facilities required for a field development on the basis of pressure
maintenance through nitrogen injection.
5. This section briefly describes the effectiveness of using water as an injection medium to achieve
reduced seismicity through pressure maintenance.
Pressure maintenance is expected to arrest reservoir compaction, and hence to reduce the seismic hazard.
However, to what extent is yet to be established through further geomechanical modelling. This work is
documented in a separate report (Ref. 1) and was carried out by URC Houston (XoM) and NAM/Shell.
Another area of further study is the impact on seismicity when injecting into or close to a fault.

12.2

References:

1. Report to the Technical Guidance Committee (TBO) on Production Measures Part 1: Depletion
Scenarios and Hazard Analysis, NAM, November 2013.

12.3

Selection of the injection medium

Pressure maintenance as a general concept is based on reinjection of a volume equal to the gas volume
production from the reservoir. By balancing the injected and produced volume, the pressure in the gas
reservoir can be stabilised. With the stabilised reservoir pressure, compaction of the reservoir rock will halt
after some time. Since compaction is thought to be a key factor in the creation of seismicity, this would also
reduce seismic activity in the Groningen area.

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N2 - Injection

Figure 12.1

Gas Production

Principle of pressure maintenance by injection.

The medium to be injected needs to meet several criteria. First, this substance either needs to be a liquid or
a gas to allow injection into the reservoir rock. Second, it should be available in large quantities at
acceptable cost to warrant an economically feasible development scenario.
An initial screening of injection media showed that water is the only liquid available in sufficiently large
quantities. The use of water injection for pressure maintenance is discussed in Section 5 of this report.
Several gases can in principle be used as an injection agent for pressure maintenance. These are either
natural components of the air or products from combustion processes:






Nitrogen (N2),
Carbon dioxide (CO2),
Flue gas
Air

Each of these gases has its advantages and disadvantages. An initial screening of these gases for the
Groningen field is provided below.

12.4

Nitrogen (N2)

Nitrogen (N2) is a colourless gas which constitutes around 78% of the volume of the Earth’s atmosphere.
The gas is mostly inert and non-corrosive. Therefore no special metallurgy measures are required to avoid
corrosion of pipelines, wells and facilities. In fact, nitrogen is already present in small quantities (14%) in
the Groningen gas.
Nitrogen can, in principle, be sourced in large quantities from the air using a cryogenic process. This will
require industrial size facilities described in Section 3. The non-corrosive nature of nitrogen helps in
achieving an efficient production and relatively safe operation process.
As nitrogen is less compressible than Groningen gas and other injection media, it provides a higher
reservoir displacement volume per standard volume of nitrogen injected than any of the other injection
gases. Therefore it has the lowest volume requirement for pressure maintenance. A first calculation shows
that some 23 wells are required for nitrogen injection. This number is based on the total amount of gas to
be replaced, the injection capacity of a Norg big-bore well, and of specific gas properties such as
compressibility and corrosion potential. Additional surfaces facilities include a dedicated nitrogen pipeline
network to transport the gas from the production plant to the injection wells
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After breakthrough of the nitrogen in the production wells, it needs to be removed again to bring the
produced gas back to sales specification. This requires a cryogenic process of a smaller scale, but on each
of the production clusters instead of a centralized plant. Compressors and flowlines are required to
evacuate the produced nitrogen to the injection wells.

12.5

Carbon dioxide (CO2)

Carbon dioxide (CO2) can be sourced from the combustion of coal or natural gas (from electricity
generation or other processes) using air separation prior to burning. This is similar to the clean coal
technology. However, the annual carbon dioxide volume required for injection compares well with the
total volume generated by all current power generation facilities in The Netherlands. It is not realistic to
assume that this total volume can be made available for injection. Furthermore, the carbon dioxide needs
to be cleaned to remove remaining impurities.
The corrosive nature of carbon dioxide (in combination with water vapour) requires gas to be removed
from the production stream. This can be done with a knock out drum to take out the liquids followed by an
electric heater (or membrane pre-treatment). Gas is then routed to membranes, followed by an amine
polishing unit.
The injection facilities are similar to the nitrogen case. Field distribution requires a dedicated carbon
dioxide distribution pipeline network and a pipeline to a source. The pipeline network and completions in
the injection and (after breakthrough) 295 production wells needs to consist of special corrosion resistant
materials. Alternatively, the gas can be dried in which case carbon steel pipe is suitable. Based on its
specific properties, the injection of carbon dioxide requires more wells than for nitrogen.
In the US, some experience was gained with carbon dioxide used for enhanced oil recovery. However, the
gas is in this case sourced from carbon dioxide fields which are not available in North-Western Europe.

12.6

Flue Gas and Air

Flue gas is exhaust gas from power plants, gas turbines, gas engines or heaters. It can also be sourced
from the combustion of coal or natural gas. The flue gas volumes generated are considerably larger than
that of carbondioxide and no air separation is required. However it is very corrosive since it contains
NOx, SOx, CO, CO2, H2O, and O2. Therefore, after breakthrough in the production wells, the flue gas will
need to be removed from the production stream before sales. Especially for oxygen the sales specification
is very tight making it difficult to reach the required levels. Other projects using flue gas have experienced
problems related to the corrosive nature of the gas.
Once passed through the reservoir and having been in contact with water, the wet flue gas will become
even more corrosive. Therefore, it needs to be removed from the production stream which requires a
cryogenic unit Injection facilities are similar to the nitrogen case. Field wide distribution requires a
dedicated flue gas pipeline network and a flowline from the production cluster into the network. Also in this
case, the pipelines and completions in the injection and (after breakthrough) production wells need to have
a special corrosion resistant materials. The complex composition of the gas makes the selection of the
corrosion resistant material much more difficult than in the case of carbon dioxide.
The corrosive nature of the flue gas may lead to a longer term safety issues. Additional explosion risks
associated with the presence of high pressure oxygen in the pipeline system will also need to be addressed.
In case of air injection, an additional risk is the reaction between the gas and oxygen in the reservoir
(internal combustion). This is sometimes used as an EOR method in oil reservoirs. Here the heat generated
by the internal combustion helps to reduce the oil viscosity.

12.7

Conclusion

Table 12.1 Lists the main selection criteria of the injection medium discussed above. It is clear that nitrogen
has advantages above all the other options. These include availability, non-corrosive nature, cost, handling
infrastructure and environmental and safety issues. For the same reasons, nitrogen has been chosen and

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successfully applied for more than 20 years in pressure maintenance and enhancing reservoir-drive
mechanisms for both miscible and immiscible oil recovery projects
Table 12.1

Options table for selection of the injection medium.

Nitrogen

Carbondioxide

Flue gas and
Oxygen

Air

Abundance

Relative cost of generation in large
quantities
Corrosiveness

Reaction with reservoir and internal
combustion with the reservoir gas.

12.8

Gas recovery with nitrogen injection.

12.9

Introduction

There is considerable experience in the petroleum industry around the world and within Shell with the
injection of nitrogen in reservoirs. However, it is mostly injected for Enhanced Oil Recovery or for pressure
maintenance purposes of gas condensate fields. The De Wijk field in the Netherlands is one of the few
examples where nitrogen is injected into a gas reservoir for Enhanced Gas Recovery, but this project is still
in its early days.
The largest nitrogen production unit in the world is located in the Cantarell Field in Mexico (ref. 1 to 9).
This is the fifth largest oil field in the world. Gas is injected to stabilise the pressure in the gas cap and
thereby increase the oil recovery from the field. The scope of the pressure maintenance case in the
Groningen field is different, but the Air Separation Units of the Cantarell project may serve as a good
analogue for what is required in the northern Netherlands.
A first estimate is that the capacity of Air Separation Units for pressure maintenance in the Groningen field
needs to be 5 to 7 times larger than the capacity of the Cantarell facilities.

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Figure 12.2

The fifth largest producing field in the world is the Cantarell complex in Mexico. It lies 85 km from
Ciudad del Carmen and is operated by Pemex. The facilities in the Groningen field need to be 5-7
times larger.

12.10 Objectives of the subsurface study
A screening and early feasibility study has been carried out on nitrogen injection and pressure
maintenance in the Groningen field. The existing Groningen history matched dynamic model was used for
this purpose, and the experience gained in simulating multiple composition gases in modelling of the Norg
and Grijpskerk underground gas storages was incorporated. Additional experience is being gained in the
De Wijk nitrogen injection project.
The impact on field reservoir pressure distribution and economic recovery was assessed. To date, the
Groningen reservoir has demonstrated “tank like” behaviour, which means that analytical methods such as
material balance models can be applied. However, since a history matched model of the Groningen field is
already available, it was chosen to take a simulation model approach. This has the following advantages:



Geological (local heterogeneity) and structural (faults) features of the reservoir can be taken into
account,




The number of required wells, their locations and the surface facilitiescan be optomized,
A more accurate representation of the gas-gas mixing PVT behaviour can be incorporated.

12.11 Basis for modelling
Modelling of a gas injection production mechanism is challenging, especially because historically the field
has been produced under depletion drive. Thermodynamically, miscibility is defined as the condition when
two fluids are mixed in any proportion and resulting mixture is a single phase. Therefore by injecting in the
reservoir, a first contact miscible displacement process of the natural gas by the nitrogen will take place,
since both fluids are in gaseous phase in reservoir conditions. Moreover, there is already a 14% volume
fraction of nitrogen in the Groningen gas composition. The effectiveness of the sweep mechanism is
controlled by mixing of the two gases by diffusion, by gravity segregation and by the impact of geological
heterogeneities.
Practical field observations and experience are very important to provide confidence in the predicted
behaviour of the reservoir. The Groningen asset operates two underground gas storage (UGS) projects Norg and Grijpskerk, where gas is stored in the same Rotliegend reservoir as in the Groningen field. In the
underground gas storages gas is injected during the summer and produced back during cold winter days.
Similar reservoir processes take place with the injection of nitrogen for pressure maintenance. Hence, the
experience in modelling of gas-gas mixing in Norg and Grijpskerk can also be used for Groningen
nitrogen injection. Specific aspects include:




Matching the Grijpskerk tracer test from 1997 (Figure 12.3),
Matching the Norg tracer test from 2008,

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

Modelling of the Norg Leaning Process, which is the displacement of indigenous Norg gas by injected
Groningen gas using the Norg UGS model (Figure 12.4). To make this displacement process more
efficient, a well was drilled in the North of the Norg field in 2012. Accurate modelling of this process
is important as only the gas at Groningen specification (i.e. unpolluted with indigenous Norg gas) can
be used as working volume for the storage.

There are two options for modelling gas injection in the reservoir simulation tool, without using a fully
compositional simulator. These are passive and active tracers:



Passive tracers-are fluid constituents which do not, in any way, influence the properties of the host
fluid. Passive tracers can be considered as a colour or dye which is put into the reservoir fluids to track
fluid flow. The passive tracer equations are solved by a separate solver. They have the option to
reduce the numerical dispersion and take less calculation time.



Active tracers (PVT tracking) do influence the properties of the fluid with which they travel and are used
for interpolation between different PVT models. When different types of fluids mix, intermediate PVT
descriptions are used, which correspond with the resulting new tracer concentration.
The use of active tracers helps in more accurate fluid modelling.

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Grijpskerk

Figure 12.3

A tracer test was performed in the Grijpskerk field to investigate gas – gas mixing in the reservoir.
Wobbe Index comparison (2% error)

43

44

45

46

47

48

49

4590
4600
4610
4620
4630
4640
4650
4660
4670

MDRT, m

4680
4690
4700

Simulated
MDT
Groningen

Indigenous

4710
4720

4730
4740
4750
4760
4770
4780
4790
4800

Figure 12.4

The experience with these models gives additional confidence in the modelling of the nitrogen
displacement process in the Groningen field.

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12.12 Simulation workflow
Simulation techniques used in the Norg UGS were applied to simple box models in order to demonstrate
the strengths and weaknesses of the passive and active tracer modelling methods. Both methods were
implemented on the Full Field Model (FFM) depending on the phase of the modelling process. Passive
tracers were used for screening purposes and the selection of a base case. They were also used for the runs
which were coupled with IPSM (Integrated Production Surface Modelling) tool. Subsequently, active tracers
were implemented to capture the gas-gas mixing process.
12.12.1 Test model
A “test model” was used to review the main differences between the two tracer options, and to better
understand their underlying assumptions. The following sensitivities were performed on the test model:





Timestep and grid dimension sensitivity
Sensitivity on the number of “intermediate” PVT models
Numerical dispersion and diffusion factors

The test model was initialised using the Groningen PVT Model (dry gas phase). The properties of the
injection fluid, i.e. nitrogen at surface and reservoir conditions, were generated using PVTSIM tool (Figure
12.5). Additionally, the properties of the synthetic mixtures of Groningen gas and nitrogen (Figure 12.5)
were simulated in PVTSIM.
250

1.3

@ 100°C

1.25
200

1.15

150

Groningen
20%
40%

Groningen
Z -factor

Pressure, bar

1.2

20%

1.1

40%

60%

100

60%

1.05

80%

80%

Nitrogen

Nitrogen
1

50
0.95

0
-200

0.9
-150

-100
Temperature, DegC

-50

0

0

350

50

100

150

200
250
Pressure, bar

300

350

400

450

0.035

@ 100°C

300
0.03

Groningen

200

20%
40%
150

60%

Viscosity, cP

Density, kg/m3

250
0.025

Groningen
20%
40%
60%

0.02

80%

80%

Nitrogen

100

@ 100°C

50

Nitrogen
0.015

0

0.01
0

50

100

150

Figure 12.5

200
250
Pressure, bar

300

350

400

450

0

50

100

150

200
250
Pressure, bar

300

350

400

450

Properties of the injected and produced gas.

From Figure 12.6 it can be observed that using active tracers results in an earlier nitrogen breakthrough,
because of the numerical dispersion. Numerical dispersion reduces the breakthrough time depending on
the simulation grid dimensions. However, the geological detail captured in the numerical models is in most
cases more homogeneous than in reality. This leads to a potential underprediction of the geological
dispersion in the reservoir. These two factors might compensate one another to a certain extent. Since with
passive tracers only host fluid exists, (in this case the lighter Groningen gas), it overestimates production
and injection potentials to the same extent as the host and injected fluid mobility difference (Figure 12.6).
The breakthrough time in both cases is a function of the grid cell and time step size. Here, the experience

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gained with the calibration of the Grijpskerk and Norg models against actual observed reservoir dispersion
becomes vital, even more because all three fields (Norg, Grijspkerk and Groningen) produce from the
same Rotliegend sandstone reservoir.
N2 breakthrough

Cumulative gas
production

Gas production
potential

N2 breakthrough

Cumulative gas
injection

Gas injection
potential

Figure 12.6

Comparison of breakthrough of injected nitrogen in test model.

The sensitivity on the number of intermediate PVT models with active tracers in Figure 12.7 shows only a
minor effect on the simulation results. This is mainly due to the fact that the property changes between
Groningen gas and nitrogen are linear. However, the simulator run time with higher number of PVT
models increases significantly (Table 12.2). The impact of using the dispersion factors from the Grijpskerk
field in the passive tracer option is shown in Figure 12.7. The default value in the simulator is already very
close to the one derived from the tracer test in Grijpskerk.

No intermediate models

No dispersion

1 inter. models

GRK dispersion

4 inter. models
Comparison of the
cases with zero, 1 and
4 intermediate PVT
models

Figure 12.7

Impact of using
“dispersion factors”
matched on GRK
model tracer test

Comparison of breakthrough of injected nitrogen in test model.

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Table 12.2

Simulator run time with higher number of PVT models.

Active

Passive

CPU time, sec

CPU time, sec

Test

727

625

Test
(with
4
intermediate PVT
models)
FFM

1101

Model

70877

28339

It was decided to use passive tracers for the screening phase because of the run time benefits. The active
tracers were implemented after selection of the basecase.
12.12.2 Full Field Model (FFM)
The Groningen FFM was completed in 2012 and consists of more than 240 thousand active cells. The
main reservoir properties are shown in Table 12.3. The well stock consists of 297 producers and 2 water
disposal injectors.
Table 12.3

Main reservoir properties of the Rotliegend reservoir in the Groningen field.

Permeability (mD)
Porosity( %)
Initial Pres @ 2875 m (Bar)
Initial Tres @ 2875 m (°C)
Current Pres @ 2875 m (Bar)
Well stock, producers
Well stock, water disposal

150
15
345
100
95
297
2

The Groningen reservoir fluid is a dry gas with approximately 82 mol% of methane (CH4), 0.9 mol% of
carbon dioxide (CO2) and 14.2 mol% of nitrogen (N2). Gas density at normal conditions (0°C and 1.01
Bar) is 0.835 kg/m3. The main properties of the Groningen gas and nitrogen are shown in Table 12.4.
Density and viscosity are slightly higher for nitrogen, but the difference is marginal.
There are several PVT models in the FFM depending on the depth and the location. So called “pseudo
thermal” initialisation was used, i.e. a separate PVT model for every 1°C temperature change. However,
for active tracers only one PVT model was considered which corresponds to Groningen gas at 100°C.
Table 12.4

Properties of Groningen gas and Nitrogen compared.

Groningen gas

Nitrogen

0.835

1.25

0.174

0.235

Viscosity (@ 0°C, 1.01 Bar), cp

0.011

0.0165

Viscosity (@ 100°C, 300 Bar), cp

0.023

0.028

Formation volume factor (@ 100°C, 300 Bar),
sm3/rm3

0.00476

0.00531

Density (@ 0°C, 1.01 Bar), kg/m3
Density (@ 100°C, 300 Bar), kg/m

3

Reservoir properties such as horizontal and vertical permeability are crucial factors for gas injection.
Because gas breakthrough time and sweep efficiency are the key parameters in the field development plan,
it is very important that the upscaled model is a good representation of the detailed geological model.
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Several grid size sensitivities were done to assess the impact. Figure 12.8 shows a comparison of the well
log versus detailed (blue line) and upscaled (red line) grid resolution from the simulation model. This
comparison shows that the current simulation model is also fit for purpose for gas injection.

Figure 12.8

Comparison of reservoir properties measured in the wells and used in the model after upscaling.

Since the purpose of the injection is to maintain the pressure, a voidage replacement scheme was chosen
as the base case development scheme. This means that the reservoir volume of the produced gas needs to
be replaced by injected nitrogen.
The start of injection time was assumed to be January 2020 for the case which evaluates the effect on the
reserves. (Figure 12.12) According to the Air Products report on the surface facilities required for the
nitrogen injection the first gas for the injection will be available in 2018 on some of the clusters and a full
injection capacity could be achieved in 2023. Therefore in the simulation model the middle of that period
was chosen as a start date.
For the rest of the sensitivities the injection start date is always January 2017.
The number of required injection wells was derived by dividing the total field gas production rate by the
maximum injection rate of the nitrogen injector.
Norg big bore capacity wells were taken as analogues for Groningen nitrogen injectors in terms of
completion and pressure and rate constraints. Provisional injector locations are shown in Figure 12.9.
These need to be optimized, but for now it has been assumed that each of the 23 injector wells will need to
be drilled from a separate new location.

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Water disposal
wells

Figure 12.9

View of the Groningen model with the 23 injector wells locations indicated.

12.13 Results and observations
For the reserves evaluation, nitrogen injection was modelled using an IPSM (Integrated Production System
Modelling) tool, which consist of surface network modelling tool coupled to a reservoir simulator. This was
required to honour the existing and future facility constraints in the Groningen system, including:






First stage compression
Summer – winter gas demand variations
Fluctuations in response to UGS activity
Gas consumed in operation

Simulations using the IPSM tool are very time consuming and difficult to interpret. Therefore, only forecasts
with the reservoir model (and without the production facility model) were used to test sensitivities, and to
generate forecasts taking 2nd and 3rd stage compression into consideration. In the runs with IPSM model
the passive tracer option was used, for simulation time reasons.
A comparison of the average reservoir pressure is shown in Figure 12.10. The pressure is stabilised at
around 60 -70 bar in the injection case. Pressure distribution maps are shown in Figure 12.11. at different
time steps. The locations of the injection wells can potentially still be further optimized.

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Figure 12.10 Comparison of the pressure development over time for the continued depletion drive development case
and the pressure maintenance development based on nitrogen injection. The reservoir pressure for the
latter case is stabilised at around 60 - 70 bar.
2020

2040

2060

No injection
30

N2 injection

200

Figure 12.11 Maps of areal pressure distribution in reservoir layer 7 for three time snapshots (2020, 2040 and
2060). The reservoir pressure maps for continued depletion and pressure maintenance are shown.

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Figure 12.12 shows a comparison of production profiles for the base case vand the nitrogen injection case.
The economic threshold for nitrogen is calculated using De Wijk example. It is shown with an economic
cut-off of 75% N2 cut the loss of reserves is around 110 BCM. An optimisation of the nitrogen injection in
individual wells in response to the nitrogen breakthrough in production cluster wells may lead to a further
reduction of the reserves losses.

Nitrogen cut threshold of 75%

N2 cut
No injection

N2 cut

Cumulative Production, m3

With injection

Loss of about 110 BCM

Figure 12.12 Comparison of the cumulative production profile for the depletion development (no injection) and the
pressure maintenance development (with nitrogen injection). The nitrogen cut due to breakthrough of
the injected nitrogen gas is also shown.

The same sensitivity on the cell dimensions as was done for the test model was also performed on the FFM.
The results in Figure 12.14 show that on a large scale the difference is negligible.

N2 cut fine
N2 cut coarse

Gas cum coarse

N2 cut

Cumulative Production, m3

Gas cum fine

Figure 12.13 Sensitivity of nitrogen injection for the grid size used in the reservoir simulation. Both the gas production
and the nitrogen cut at the producer wells are not significantly affected by the cell dimensions.

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12.14 Alternative Development Options
Various sensitivities were tested in the full field model of the Groningen field to assess the merits of different
development options:





Partial voidage replacement
Injection wells located in the northern part of the field only
Different start up times for the injection

12.14.1 Partial Voidage Replacement
In the base case development, full voidage replacement was maintained throughout the production period.
This means that the reservoir volume of injected nitrogen gas was kept equal to the volume of gas
withdrawn from the reservoir. In case of partial volume replacement, only a fraction of the withdrawn
reservoir gas volume is replaced by nitrogen injection. In this development option, the reservoir pressure
decline is slowed down, reducing the compaction rate compared to the depletion drive scenario.

Cum. gas
production
100%
50%
70%
90%
No inj

N2 cut

N2 cut

Cumulative Production, m3

Production profiles of different partial voidage replacement cases are illustrated in Figure 12.15.
Expectedly, the smallest loss of gas recovery occurs with minimum injection which is chosen to be 50% of
the production. However, in this case the reservoir pressure declines more than for the other partial
voidage replacement cases throughout the production period (Figure 12.16) and the impact on compaction
and seismicity is therefore also reduced.

100%
50%
70%
90%

Figure 12.14 Cumulative production and nitrogen cut of the production for different partial voidage replacement
development options.

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No inj

100%

90%
Year 2030
30

70%

60%

50%

200

Figure 12.15 Reservoir pressure distribution (reservoir layer 7) in 2013 for different voidage replacement scenario’s.
The depletion scenario and voidage replacement at 100%, 90%, 70%, 60% and 50% are shown.

12.14.2 Injection in the northern part of the field only
In the base development scenario, the injection wells are located throughout the field, at larger distances
from the production clusters to achieve an effective displacement of the Groningen gas towards the
production clusters. A streamline display shows the displacement paths from injection point to the
production cluster of the gas (Figure 12.17). Initial screening showed the well placement is effective, but
can be further optimised.

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Figure 12.16 Streamlines for nitrogen from the injector wells to the production clusters. This plot has been used as a
tool to optimise the injector locations.

The majority of the production clusters in the Groningen field is located in the southern part of the field. In
section 3.2, the historical development of the field is discussed. The denser development of the southern
area has a clear impact on the reservoir pressure while producing under depletion. This suggests an
alternative development scheme for pressure maintenance, where nitrogen is injected exclusively in the
north of the field and gas is swept towards the southern production clusters.
This scenario requires a less extensive nitrogen pipeline network and would results in a North to South
displacement of the gas. Of course, the northern clusters would start producing mostly nitrogen early on.
The scenario with northern injection wells only is shown in Figure 12.18. The pressure distribution is much
less uniform between North and South. This will probably reduce the effectiveness of this option in
reducing seismicity. However, the production profile looks more attractive comparing with the full injection
case. (Figure 12.19). Also, the pressure decline in the north is reduced, which is the area with the largest
seismic risk. This may have a positive effect on the total seismic hazard.

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2020

2040

2060

No injection
30

N2 injection

200

Figure 12.17 Reservoir pressure distribution (in reservoir layer 7) over the field for both the depletion case (above)
and the (below) case with nitrogen injection exclusively in the north of the field (below).

Figure 12.18 Production profiles for three development options; (1) depletion (green), (2) nitrogen injection in wells
throughout the field (red) and (3) nitrogen injection wells exclusively in the North (brown).

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12.14.3 Different start up times for the injection

Cum. gas
production

N2 cut

2017
2019
2021
2023
No inj.

Figure 12.19

N2 cut

Cumulative Production, m3

The earliest possible start of nitrogen injection in the Groningen field is in 2018, as will be explained in
section 4. The effect of a later start was also assessed. Figure 12.19 shows results for starting injection in
2017, 2019, 2021 and 2023, respectively. It is obvious that delaying the injection start results in a higher
gas recovery, but also in a delay in the reduction of reservoir compaction.

2017
2019
2021
2023

Cumulative gas production and nitrogen cut for scenarios with different start of
nitrogen injection. Later start of injection leads to a reduced loss of reserves.
Continuation of depletion without injection (green) results in the largest recovery from
the field.

12.14.4 Summary of the development options.
During the simulation phase on the full field simulation model
scenarios were considered:

the following alternative development



Partial voidage replacement options with the range from 50% to 90% reinjection of produced volume
by nitrogen. The results suggest that the least voidage replacement option (50%) will have a minimum
negative impact on the reserves. However, it also will have a reduced effect on the compaction i.e.
more pressure decline compared to the other cases.



A development plan based on injection exclusively in the northern part of the field results in a less
uniform pressure distribution, but though it has a more attractive production profile (than the base
case pressure maintenance development). Due to the different approach to areal sweep fewer
injection locations in the reservoir are required. This allows the number of wells to be reduced in case
of lower initial injection volumes and partial volume replacement.



Different start-up times for the injection from 2017 to 2023 were also analyzed. The earliest injection
start-up time yield in the lowest ultimate recovery, although the reservoir pressure stabilizes at higher
value.

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12.15 Development plan for pressure maintenance using nitrogen injection
12.16 Technical premises
This section describes the surface requirements for nitrogen injection in the Groningen field. The facility
scope will be described, including a project execution strategy and the operations / maintenance of the
nitrogen injection facilities.
In order to maintain the reservoir pressure in the Groningen field, new injection wells have to be drilled in
the province of Groningen, in approximately the same area where the production clusters and pipeline
infrastructure are located. Additional facilities are required for the production and transport of nitrogen.
The new infrastructure scope consists of:





Injection wells to be drilled from 23 new locations (see section 12.4).



Pipeline and compression infrastructure for transporting the nitrogen from the ASU’s to the injection
wells and from the NRU’s to the injection wells.



Nitrogen supply systems (so called ASU – Air Separation Units).
Nitrogen Rejection Units (NRU; to separate excess nitrogen from the produced gas to bring the gas
back to Groningen quality before delivery into the GTS pipeline system. The nitrogen from the NRU’s
will be used for reinjection).

Electrical infrastructure for the ASU’s and NRU’s.

The project execution (construction) is expected to take approximately 6 years and Front-end activities
(selection of facilities and design) about 2 years. First injection could start in 2018 and full injection 2023.

12.17 Non-technical premises
The surface facilities (construction and permanent presence during operation), pipelines (construction –
crossing canals/highways) and drilling of new wells will have a large impact on the landscape and
therefore on local communities.
A large electrical power consumption is required, which will have a big impact on CO 2 emissions.
Currently, it is assumed that the electrical power will be supplied by power plants. This can be optimized in
the future using synergy with concepts having a lower carbon footprint. Potentially, the Eemshaven power
plant can be utilised.
It has been assumed that seawater cooling will be used for facilities close to the coast, which can also
impact the seawater temperature. This impact also has to be assessed for future development. Cooling for
the NRU’s is assumed to be done with air coolers.

12.18 Assumptions
The initial screening concept has been based on certain assumptions already captured in section 12.16
and section 12.17. These assumptions and others are described here with some relevant background
information.
Table 12.5

Assumptions table for the pressure maintenance development plan for the Groningen field based on
nitrogen injection.

Assumption

Background

Nitrogen will be produced through up-scaling of a
proven technology (cryogenic process).

The cryogenic process is an established process
used worldwide in different industries and also for
injection in reservoirs (including Cantarell, Mexico
and NAM De Wijk).

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The base case is an injection rate equal to the
Groningen yearly production of:
1) 42 BCM/year (base case)
2) 30 BCM/year (option)
with an injection pressure of 130 bar.

The earliest start date for injection is in 2018 and
full injection rate can be achieved in 2023. The
Groningen production and related required
injection pressure is expected to be at that level in
2023. This has to be assessed in more detail in a
next phase.

23 Injection wells spread out over the field area are
required.

See section 12.4

The composition of the produced gas has to remain
on Groningen specification.
Injected nitrogen
breakthrough in the reservoir will take place and
excess nitrogen has to be removed. The excess
nitrogen will be re-injected in the injection wells.

No changes in the required gas composition on the
European market are foreseen LoCal gas).

The ASU’s will be grouped together in the vicinity of
the Eemshaven.

 The environmental impact is lower in the
Eemshaven area than on the countryside.

 Seawater cooling can be used.
 Power plants are located in the Eemshaven.
An NRU will be located in the vicinity of each
production cluster.

 Minimal transport distance between excess
nitrogen and reinjection wells.

 The maximum height of an NRU is restricted to
25 m to limit the environmental impact.

The pipelines will be located as close to the existing
infrastructure as possible.

Minimal impact on permits because of the bundling
principle.

Power plants (electricity supply) and electrical
infrastructure for the NRU’s will be provided by
utility companies. Additional connections and
transformers will be included in this project.

In line with the Groningen
infrastructure philosophy.

field

electrical

12.19 Development concept
12.19.1 Plot lay-out
The plot lay-out is given in Figure 12.19. The 23 injection wells (light blue dots) are located throughout the
Groningen field. The ASU plant is located in the North of the province next to the Eemshaven. The
pipeline infrastructure (light blue lines) transports the nitrogen from the ASU to the injection wells. The
nitrogen distribution pipeline is split in a western and eastern section connecting the production clusters
(dark blue dots) as the existing production pipeline ring. The injection wells are connected to the nitrogen
distribution network (red). Next to each production cluster an NRU is located. The NRU is connected to the
main pipeline via a short pipeline.
The pipelines have to cross canals and highways, which will complicate both the permits and execution.

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Figure 12.20 Plot lay-out of the ASU plant, N2 injection facilities, NRU’s wells and pipelines in the Groningen
province

12.19.2 Injection wells
The scope of the injection wells is as follows:





The injection well designs are based on the Norg injection wells (9 5/8” tubing).
required to drill these wells.

A large rig is

New land has to be leased for each injection well.
Civil/electrical infrastructure and hook-up to the pipeline. The hook-up concept is based on De Wijk,
up-scaled to the required flow rate.

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12.19.3 Air Separation Units (ASU)
The ASU concept has been developed by a large supplier of ASU’s. The ASU comprises the following
scope:






The nitrogen production rate will start with 30 to42 BCM/year declining in the future as excess
nitrogen from the produced gas will be re-injected.
The plant consists of 9 to12 parallel ASU’s (for the 30 and 42 BCM/y case, respectively), which
requires a flexible production rate. Compressors for separation of nitrogen/oxygen and for
compression to the distribution pipelines are provided with a housing to comply with noise regulations.
All ASU’s are located on single plot space (450 x 270 m) with a maximum height of 62 m (cold
boxes). This land has to be leased.
The maximum power requirement is 1000 to1416 MW (30 and 42 BCM/year cases), which is in the
range of powerplants at the Eemshaven. A 380 kV connection to Eemshaven Oudeschip (utility
company connection point) is required.

Artist impressions of the ASU plant can be found below (Figure 12.21 to Figure 12.23). This gives an idea
of the size of the ASU plants. The power plant shown depicts the one already present at Eemshaven
industrial area (without power lines) in relation to potential ASU installation. The power plant shown
provides an example and is similar to the one already present in the Eemshaven industrial area. Note the
exact ASU complex location will be subject to change. Also the power cable poles are not shown and
therefore the exact location of the ASU plant will probably change.

Figure 12.21 ASU plant view from the south

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Figure 12.22 ASU plant aerial view

Figure 12.23 ASU plant side view (with existing power plant to the left)

12.19.4 Nitrogen Rejection Unit (NRU)









The NRU concept has also been developed by a service company. An NRU is required for each gas
processing unit and therefore 20 NRU’s are required. Each NRU comprises the following scope:
The nitrogen breakthrough in the produced gas is limited at the start of injection but will increase with
time. Therefore, the NRU concept will be able to process a variable nitrogen content and flow rate.
Pre-treatment (removal of mercury, carbon dioxide, water, solids).
The plot space of an NRU is 200 x 100 m and the maximum height (cold box) is 25 m. This land has
to be leased.
The maximum power requirement is 32 MW per NRU. Additional 110 kV rings to substations and new
transformers are required.
Compressors for the separation of nitrogen and natural gas and for compression to the distribution
pipelines are provided with a housing to comply with noise regulations.
Air coolers are used for cooling similar to the De Wijk ASU design.

Note:

The nitrogen facilities study was based on the removal of maximum 50 mass% nitrogen. Latest
study work indicates that this should be maximum 80 mass%. Cost and energy use have been
scaled to reflect this.

An artist impression is shown in Figure 12.24.

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Figure 12.24 NRU plant aerial view (with existing production facility to the left)

Figure 12.25 NRU plant aerial view (with existing production facility to the right)

12.19.5 Pipeline infrastructure
The pipeline infrastructure is divided in a western and an eastern line, which connect in the south to form a
ring. The east and west lines do not individually need to transport the full capacity as a 100% availability
is not assumed to be required. The dimensions of the pipelines are based on that assumption. The total
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length of the pipeline ring is around 120 km with side connections to the injection wells and NRU’s.
Carbon steel has been selected as material for the pipelines.
12.19.6 Project Execution planning
At least 2 years are required to investigate several concepts, select the final concept and design. The
execution (construction) could start earliest in 2016 and is expected to take 6 years to complete. Injection of
nitrogen could start earliest in 2018 and is expected to reach full injection rate of 30 to 42 BCM/year by
2023.
The project execution planning can be found in the Table 12.6 below:

Table 12.6

Notional high level Project schedule. Indicated is the number of completed units (cumulative). For
example, 3 wells are drilled and hooked-up in 2017, 11 in 2019 and the 23 wells are all finished in
2022.
2014

2015

2016

2017

Feasibility, Concept Select and FEED
Final Investment Decision
Injection wells drilling
Injection wells civil
Injection wells hook-up

2018

2019

2020

2021

2022

3
3
3

7
7
7

11
11
11

15
15
15

19
19
19

23
23
23

ASU AP
ASU civil

1

3

6

8

10

12

NRU
NRU civil
NRU hook-up

1

4

8

12

16

20

Pipeline infrastructure

Electrical infrastructure

12.19.7 Operations and maintenance
The ASU’s and NRU’s typically have an expected lifetime of 15 years and need a 4-yearly maintenance
shutdown. There is an option for the ASU’s and NRU’s to be owned, operated and maintained by the
vendor.
The electricity power requirement is:
ASU’s: 1416 MW (42 BCM/year) or 1000 MW (30 BCM/year) at maximum use
NRU’s: 640 MW at maximum usage
An important aspect is the flexibility requirement for the NRU plants. The export gas has to be maintained
at Groningen quality specifications for delivery into the GTS pipeline system. Changes and disturbances
can be caused by trips/maintenance/start-up of the upstream production facility, start-up/ramp-up of the
NRU itself and the change in the nitrogen content of the produced gas. This requires further in-depth study
of the concept, taking into account control and automation requirements and optimising the concept of the
NRU’s in combination with the ASU plant.
12.19.8 High level development cost estimate (screening level)
The cost estimate is preliminary and subject to a large potential variation, due to unforeseen scope growth,
complexity and/or permit requirements.

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The CAPEX cost estimates are given below for the 30 BCM/year and 42 BCM/year cases:
30 BCM/year case

42 BCM/year case

CAPEX (nominal) bln EUR MOD

CAPEX (nominal) bln EUR MOD

Injection wells drilling
11%
Pipeline infrastructure 15%
ASU, incl. water intake 25%
NRU
45%
Electrical infrastructure 3%

Injection wells drilling
11%
Pipeline infrastructure 16%
ASU, incl. water intake 30%
NRU
40%
Electrical infrastructure 3%

Total

5.7 – 8.5

Total

Table 12.7

Nominal Capex estimates

6.5 - 10

The OPEX cost estimate is based on operations, maintenance and electricity costs. The electricity costs are
based on electricity consumption estimates by Air Products and on 2013 electricity market prices in the
Netherlands. The electricity costs will be highest in 2023 as a maximum of 30 or 42 BCM is injected. The
maximum electricity demand will be in 2018 when the injection will start. From 2023, nitrogen from the
produced gas can be reused for injection. The NRU’s require less power. The increased fuel gas
consumption to generate electricity results in a further reducing of sales gas.
The OPEX cost estimates are given below:
30 BCM/year case

42 BCM/year case

OPEX (nominal)

OPEX (nominal)

mln EUR/y

Electricity

686
Ops & Maintenance

Total

744

Table 12.8

Nominal Opex estimates.

58

mln EUR/y

Electricity

858
Ops & Maintenance

Total

930

72

12.20 Project uncertainty
This early feasibility study aims to establish the total scope of a pressure maintenance project. Therefore,
large uncertainties remain to be addressed which requires further investigation. These uncertainties
include:






Geological complexity of the reservoir may affect the nitrogen breakthrough times differently than
assumed.
Gas-gas mixing and displacement behaviour in the reservoir might be different from that observed in
the UGS fields, which were used as an analogue.
The current engineering concept is based on vast upscaling of existing concepts. Its feasibility is yet to
be demonstrated. Both timing and costs will be affected by the uncertainty in the upscaling.
Non-technical uncertainties pertain to the environmental and societal footprint associated with the
project. This is treated in section 4.7.

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12.21 Project optimisation
Further optimisation of the injection well locations may result in a higher efficiency of the displacement
process in the reservoir.









The research team in Rijswijk working on Optimised Decision Making under Uncertainty (ODMUU) has
analysed the scope for further optimisation of the nitrogen injection balance and identified various
optimisation trade-offs. For instance, nitrogen breakthrough could be delayed and nitrogen
production could be reduced at the expense of lower production rates and gas recovery losses.
Detailed economic evaluation will be required to find the preferred optimum for this trade-off.
Optimisation of injection well locations has the potential to improve the efficiency of the displacement
process.
The development scheme used in this report can potentially be optimised.
alternative for the Eemshaven location of the ASU, Delfzijl could be considered.

For instance, as an

Different scenarios with full and partial voidage replacement schemes and various injection start-up
times could be considered.
Chasing of the nitrogen with cheaper flue gas from the ASU’s or power plants in the Eemshaven
industrial area. This would need to be done with care to avoid breakthrough of the flue gas in the
production wells which at that point will be lost for production.
Scaling of the various elements of the project, particularly the nitrogen plant, could be further
optimised. Cost estimates have been prepared both for a 40 Bcm/yr production case and a 30
Bcm/yr production case. Currently, these indicate that the larger scheme is more attractive on a unit
nitrogen cost basis, but detailed engineering analysis needs to confirm this.

12.22 Environmental and societal impact
A nitrogen injection project will carry a significant environmental footprint:



Significant acreage will be required to locate the facilities.









The nitrogen generation plant will be located at a large site at Eemshaven.
The 23 injector wells will need to be drilled from new locations.
Additionally, substantial extension of current production cluster locations will be required to place
the nitrogen rejection units.

The dimensions of the required industrial complex at Eemshaven and the 20 NRU’s at gas processing
facilities throughout Groningen will result in horizon pollution in the wide Groningen landscape and
may prove to be an unacceptable visual impact for the local community and nation at large..
The compression required for producing nitrogen from air and for removal of nitrogen from the
produced gas will cause noise pollution. Gas transport may cause additional noise issues.
Installing 200 km of large diameter pipeline with crossing of roads, highways and canals will cause
significant disturbance.
The production of nitrogen and its removal from the produced gas requires significant amounts of
electrical power.




This power consumption is associated with a large carbon dioxide footprint.



Nitrogen removal limits the operational flexibility in production capacity.

Seawater cooling at Eemshaven may increase water temperatures in the Eems-Dollard estuary. This
may constitute an undesirable impact and may impact the feasibility of the project.

Due to these environmental impacts, the societal support for such a large-scale project needs to be tested
and explored.

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12.23 Gas recovery with water injection
Water injection for Enhanced Oil Recovery purposes and for the Pressure maintenance is widely used in
the world. However, it is very rare that water is injected in dry gas reservoirs. There are a number of
reasons for this:






Dry gas recovery is already quite high without any enhancing methods, because of its compressibility
(Figure 12.26).
The mobility difference between gas and water is large, so displacement would be unfavourable and
leads to trapping of significant amounts of unrecoverable hydrocarbons. 25%, 50% and 100% lines in
Figure 12.26 are combined from the sweep efficiency effect and the economic cut-off of water
production. This is just a conceptual representation of the driving mechanism and for the more accurate
results the detailed simulation study is required.
The process of gas displacement by water is immiscible. Gas will be trapped as residual saturation
behind the water front.

There are additional reasons why water injection is not attractive for the Groningen field:



The impact of water injection on seismicity is difficult to predict,. In fact it may introduce more
earthquakes because of the large pressure gradients in the field, fraccing due to thermal effects and
injection into fault planes.





Injection of sea water may result in souring of the reservoir i.e. through the formation of H2S.
Possible fault weakening due to lubrication of the fault interface which may cause a fault sliding.
The amount of water required to inject in Groningen for pressure maintenance is between 1.3 and 1.8
mln res m3/day. This volume requires between 250 and 400 injector wells. The surface facilities
required are 30 times bigger than Shell’s largest water injection asset in Bonga, Nigeria.

It can be concluded that the combination of project size, costs and potential negative impact on seismicity
makes water injection an unattractive development option for pressure maintenance in the Groningen field.

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25%

100%

50%

P/Z, Bar

345*

100
70

Current reservoir pressure
Aquifer influx
Pressure at the start of injection

Water injection
15

Abandonment pressure with depletion drive

0

Recovery factor, %

80% 85% 96.5%

Figure 12.26 The effect of water injection on the gas recovery. Also the natural aquifer influx effect is shown for
comparison sake.

12.24 Conclusions













Maintaining the reservoir pressure in the Groningen field at a level above the abandonment pressure
will after a delay period halt compaction and subsidence.
Although pressure maintenance will arrest reservoir compaction, further geomechanical modelling is
required to confirm and assess the impact on the seismic hazard. This is currently being addressed by
URC Houston (XoM) and NAM/Shell. Especially the impact on seismicity of injection into or close to a
fault is a concern (Report to the Technical Guidance Committee (TBO) on Production Measures Part 1:
Depletion Scenarios and Hazard Analysis, NAM, November 2013).
Pressure maintenance using injection of nitrogen has been studied, supported by a base case
development concept and production forecasts.
Based on its abundant presence, relatively inert behaviour and tolerance for presence in the sales gas,
nitrogen was chosen as the optimal injection medium.
Analyses have been based on the Groningen reservoir model, and on the experience in modelling of
gas–gas displacement and mixing in the reservoir in the underground storage projects of Norg and
Grijpskerk, but the remaining uncertainty in the production forecasts and recovery is large.
Breakthrough of nitrogen in producer wells as a consequence of pressure maintenance is expected to
significantly reduce Groningen gas reserves compared to continued depletion of the reservoir.
Large electricity consumption further reduces the gas volume available for sales.
The scope of the project is far beyond the scale of current global experience (5 – 7 times larger than
the largest similar project ever installed).
Although the contours and main dimensions of a Pressure Maintenance Project have been sketched,
technical feasibility of the project has not yet been established.
Installation of nitrogen injection takes at least 5 years to first injection and some 9 years to ramp up to
full capacity.

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


Cost of the project is estimated at 6 to 10 bln EUR.



This study is in an early stage assessment of the recovery concept. Optimization is still possible for a
number of aspects.



The project carries a material environmental footprint, with significant land use and electricity
consumption. Societal support for a large-scale nitrogen injection project is expected to be a key risk.

Injection of water is not considered a feasible and reliable pressure maintenance option to reduce
seismic activity.

12.25 References
Reservoir Management Issues in the Cantarell Nitrogen Injection Project, F. Rodríguez, G. Ortega, J.L.
Sánchez, O. Jiménez, PEMEX Exploración y Producción, 2001 13178-MS OTC Conference Paper
Nitrogen from Cryogenic Air Separation Process To Be Used for Pressure Maintenance and To Enhance
Recovery at Cantarell Complex in Campeche Bay, Mexico, John R. Kirtley, Bechtel, 1999 10864-MS
OTC Conference Paper
Nitrogen Injection in the Cantarell Complex: Results After Four Years of Operation, J.L. Sánchez, SPE, A.
Astudillo, F. Rodríguez, SPE, J. Morales, and A. Rodríguez, PEMEX E&P, 2005 97385-MS
SPE
Conference Paper
Implementing Convection in a Reservoir Simulator: A Key Feature in Adequately Modeling the Exploitation
of the Cantarell Complex, E. Manceau, E. Delamaide, J.C. Sabathier, S. Jullian, F. Kalaydjian, IFP; J.E.
Ladron De Guevara, J.M. Sanchez Bujanos, F.D. Rodriguez, PEMEX, 2000 59044-MS SPE Conference
Paper
Implementing Convection in a Reservoir Simulator: A Key Feature in Adequately Modeling the Exploitation
of the Cantarell Complex, E. Manceau, E. Delamaide, J.C. Sabathier, S. Jullian, F. Kalaydjian, IFP; J.E.
Ladron De Guevara, J.L. Sanchez Bujanos, F.D. Rodriguez, Pemex, 2001 71303-PA SPE Journal Paper
Overview of the Cantarell Field Development Program, T. Limón-Hernández, G. De-la-Fuente, G. GarzaPonce, Pemex Exploracion y Produccion; M. Monroy-Hernandez, Bechtel, Corp., 1999 10860-MS OTC
Conference Paper
Status of the Cantarell Field Development Program: An Overview, T. Limón-Hernández, G. Garza-Ponce
and C. Lechuga-Aguiñaga, Petroleos Mexicano (Pemex) Exploración y Producción, 2001 13175-MS
OTC Conference Paper
Mechanisms and Main Parameters Affecting Nitrogen Distribution in the Gas Cap of the Supergiant Akal
Reservoir in the Cantarell Complex, Fernando Rodr guez, PEMEX-UNAM; Jose L. Sanchez, Agustin
Galindo-Nava, PEMEX, 2004 90288-MS SPE Conference Paper
Cantarell Field: Modernization and Expansion of the Pipeline Network, J. B. de León; A. Argüelles, R.M.
Morales, Petroleos Mexicano (Pemex) Exploración Y Producción, 2001 13176-MS OTC Conference Paper

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13 Appendix C: Conclusions / Insights
The strength of an earthquake may be characterized according to the magnitude, or moment, of the
earthquake in the subsurface which is characterized by the amount and area of slip that occurs on faults
during the earthquake. Alternatively the strength of an earthquake may be measured as the magnitude of
ground motion experienced at the surface during an earthquake which is typically characterized by the
peak ground velocity (PGV) or the peak ground acceleration (PGA).
Establishing the future maximum strength of earthquakes in the Groningen gas field depends on the method
chosen to assess the maximum magnitude as well as on some remaining technical uncertainties. The key
assessment choices concern with 1) how to express the strength of an earthquake, 2) the time interval for
which the maximum magnitude is to be assessed, and 3) the appropriate statistical definition for maximum
strength.
The time interval for assessment matters for two reasons. First, a longer time interval means more
earthquakes and hence a greater chance of experiencing a higher magnitude earthquake. Second, unlike
natural earthquakes, induced seismicity has a beginning, middle, and end. This means statistics describing
the average annual number or moment of observed earthquakes is not constant in time but will vary with
gas production. For these reasons the maximum strength of a future earthquake depends on the exposure
time considered and the start time of this exposure interval relative to the cumulative gas production.
Several different definitions for the maximum strength of a future earthquake exist; each appropriate for
different purposes. These include the maximum possible magnitude, the maximum expected magnitude, i.e.
the magnitude with a 50% chance of exceedance, or the maximum magnitude with some other chance of
exceedance, e.g. a 10% chance of exceedance over 10 or 50 years are commonly seen in international
conventions adopted for reporting hazard assessments for natural seismicity.
The following sections summarise the findings of the NAM studies conducted in response to the fifth study
described in the Letter to Parliament (Kamp, 2013).

13.1

Maximum possible magnitude

Previous seismic hazard assessments considered relevant for the Groningen gas field were all influenced by
an estimate for the maximum possible magnitude of 3.9 (Crook et al., 1995; Crook, Haak, & Dost, 1998;
van Eck et al., 2006; Muntendam-Bos et al., 2008; Dost et al., 2012). Two main methods were used to
estimate this maximum possible magnitude. First, frequency-magnitude statistics of historic earthquakes
were used to estimate a cut-off magnitude for which it was considered impossible for a larger magnitude
event to ever occur regardless of exposure time. Second, compaction models and mapped fault geometries
were used to estimate the maximum possible slip and slip area. Based on the insights of the work done in
the previous months these estimates are now recognised to be unreliable due to shortcomings in the
applications of these methods. The estimates based on the frequency-magnitude statistics do not take full
account of the observational uncertainties associated with a limited number of recorded earthquakes. The
estimates based on geomechanical models do not take full account of the maximum possible area of preexisting faults given the resolution limits of reflection seismic surveys.
The recent studies reported here demonstrate that the frequency-magnitude data provide no reliable
estimate for the maximum possible magnitude due to the limitations associated with the small number of
recorded earthquakes in Groningen in accordance with a recent independent study (Muntendam-Bos &
Waal, 2013). Other methods were also investigated based on the physical limitations imposed by the finite
amount of strain energy or the finite area of pre-existing faults or from global analogues. These indicate the
maximum possible magnitude might be as large as 6.5. This does not mean that earthquakes up to this
magnitude are expected, it only illustrates that there is a physical upper limit to induced earthquake
magnitudes in the Groningen Field. If the partitioning factor were invariant with production, the maximum
magnitude of a future event is expected to be M=4.5 with a 95% upper bound of M=5.5. However the
fraction of induced strain accommodated by earthquakes is likely increasing with on-going reservoir
compaction and this explains the observed distribution of earthquakes in time and over the Groningen
area. If future seismicity follows the same trend observed in the historic earthquake catalogue, then based

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on the current analysis there is a 1 in 2 chance of M>4.2, a 1 in 10 chance of M>4.9, and a 1 in 100
chance of M>5.4 before the end of 2023.
This significantly revised estimate means that the maximum possible earthquake magnitude is unsuitable for
characterising the seismic hazard associated with future induced earthquakes as it has little or no
connection with the credible range of future seismicity. Instead, future seismicity is more appropriately
characterised by probabilistic assessments that yield estimates for the earthquake magnitude or the ground
motion with a certain probability of exceedance over a certain exposure period. Such probabilistic
estimates of seismic hazard are necessary to appropriately assess the resilience of buildings and infrastructure within the vicinity of the Groningen gas field.

13.2

Probabilistic seismological assessment

Reservoir pressure depletion induces reservoir compaction, and together they induce stress changes within
and around the reservoir. In some places the stress changes experienced by a pre-existing fault may be
sufficient to equal its resistance to slip. Under these conditions the fault is vulnerable to experiencing
earthquakes. During an earthquake, abrupt slip on a fault releases elastic strain energy stored in the
surrounding region by localizing anelastic strain along the fault.
Geomechanical models for reservoir compaction within the Groningen field are constrained by subsidence
measurements to provide estimates for the induced elastic strains that are available to induce earthquakes.
The fraction of induced strain accommodated by seismogenic fault slip cannot yet be predicted by
geomechanical models – but it can be measured in the field by comparing historic seismicity and
subsidence observations. These data indicate the historic strain fraction associated with earthquakes is very
small (0.1%) but possibly increasing with reservoir compaction. A probabilistic model for this compactiondependent strain fraction was developed and includes a wide range of uncertainty due to the limited
number of recorded earthquakes.
Over the next 10 years (2013-2023), based on a linear-elastic reservoir compaction model and the current
production plan (BP12), the expected maximum magnitude (50% chance of exceedance) is 4.2. Over the
same period there is also a 10% chance of exceeding a magnitude 4.9, and a 1% chance of exceeding a
magnitude 5.4. Subsequent 10-year periods exhibit different seismicity rates due to the changing balance
between the possible seismicity increases with further compaction and the expected seismicity decreases
with the natural decline in future production rates. All these estimates include allowances for the known
aleatory uncertainties (random variations). However, they are also contingent on the assumption that the
statistics of historic seismicity will provide a guide to the statistics of future seismicity. This epistemic
uncertainty is not trivial and motivates significant further data gathering.

13.3

Probabilistic seismic hazard assessment

Probabilistic Seismic Hazard Analysis (PSHA) was performed using two independent implementations of
standard Monte-Carlo techniques adapted to account for time variant seismicity. Taking Eurocode 8 as a
guide, these assessments focused on mapping the ground motion with a 0.2% average annual chance of
exceedance over the next 10 years (2013 to 2023). This is equivalent to the 10% chance of exceedance
within 50 years stated in Eurocode 8 for time-invariant natural seismicity, but for a reduced assessment
period of 10 years to ensure a sufficiently reliable assessment of the time-variant induced seismicity
observed within the Groningen Field.
Predicted maximum ground motions with an average annual 0.2% chance of exceedance, for the 10 years
of production from 2013 to 2023, are 22 cm/s (PGV) and 5.6 m/s2 or 0.57g (PGA), located above the
region of greatest reservoir compaction. These values were obtained using the linear compaction model,
the Akkar et al (2013) ground motion prediction equation (GMPE) modified to be consistent with ground
motion data from the Groningen Field, and by including all earthquakes of at least magnitude 1.5. Direct
comparisons with hazard maps published for other regions are to be avoided because these generally only
include M≥5 earthquakes, e.g. California or M≥4.5 earthquakes, e.g. Europe. Disaggregation of the
seismic hazard into its component parts show that only intermediate magnitude earthquakes (4 to 5)
influence the result. Earthquakes less than magnitude 4 are too small and earthquakes greater than
magnitude 5 are too rare to influence the hazard.
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13.4

Remaining technical uncertainties

Results from recent NAM studies indicate there are at least four key sources of epistemic uncertainties.
These all may be amenable to reduction in time given sufficient gathering of reliable new information.
First, the current seismic hazard assessment excludes the possibility of any triggered earthquakes that
release compaction-induced elastic strains as well as naturally occurring elastic strains due to tectonics or
some other natural process. The lack of any earthquakes in recorded human history within the vicinity of
the Groningen field prior to gas production supports this choice.
Second, the detailed mechanics of reservoir compaction and subsidence remain subject to uncertainty due
to the limited sensitivity and time sampling of historic subsidence measurements and due to the limited
length-scales and time-scales of laboratory measurements. Consequently a range of linear and non-linear
constitutive models for compaction in the Groningen field are presently consistent with the known history of
gas production and subsidence. These different models differ in their long term predictions for reservoir
compaction but only show limited variability in the next 10 years.
Third, the role of faults and the future evolution of the fraction of the elastic strain released by earthquakes
(strain partitioning factor) remain subject to uncertainties due to the relatively limited duration of historic
earthquake monitoring and the performance capabilities of the current monitoring network. The historic
earthquakes do not always occur on mapped faults; this might be due to the current level of earthquake
location uncertainty, or because the earthquakes also occurred on smaller faults below the resolution limit
of reflection seismic surveys, or because earthquakes did not always occur on pre-existing faults. The
current seismological model allows for all three possibilities – however it is possible that new information
gained from further and improved earthquake monitoring will reveal just one of these mechanisms as
dominant. Equally, the wide uncertainty range about the future strain fraction released by earthquakes may
require revision given subsequent new information about the mechanism or the rate of seismic moment
release with further reservoir compaction. The benefit of geomechanical models for predicting the future
slip behaviour of mapped faults is uncertain due to limited knowledge of the number, location, geometry
and strength of pre-existing faults that may host intermediate magnitude earthquakes. Nonetheless, such
models do offer opportunities for investigating possible scenarios for compaction-induced fault slip to aid
further understanding of the process.
More detailed geomechanical modeling with emphasis on the role of faults is in progress, but is currently
hampered by the paucity of relevant field data. which leaves room for multiple geomechanical
explanations (models). A higher resolution of more field parameters is required to make a step forward in
a better understanding of the geomechanical behavior. A data acquisition campaign is in progress,
including logging of geomechanical rock properties (Borgsweer) and installation of geophones in two deep
observation wells. Additionally, the existing monitoring network of shallow geophones and accelerometers
will be extended.
The geomechanical modeling combined with the new field data will be used to improve the understanding
of the fault slip behavior and when validated by the observed seismicity, this has the potential to reduce the
currently large epistemic uncertainties. A full field geomechanical modeling exercise is in progress
showing promising results. This should lead to a geomechanical workflow used in the seismic hazard
analysis to enable subsurface stress management as a tool to reduce the hazard.
Fourth, the relationship between an earthquake and the strength of the associated ground motions
experienced throughout the region of the Groningen field remains uncertain due to the limited number of
ground motion measurements for earthquakes of at least magnitude 3 that are necessary to constrain
current uncertainties in the scaling of ground motion with earthquake magnitude and the variability in
ground motion between different earthquakes and between different surface locations.

13.5

Future data gathering designed to reduce uncertainties

Since 1995, the current configuration of the Groningen monitoring network has provided field-wide
coverage for the detection and location of all earthquakes of at least magnitude 1.5: standard horizontal
location uncertainties are about 500 m, but typically earthquakes depths cannot be estimated and they are
then assumed to be at a depth of 3 km (see Kraaijpoel et al. 2008). Plans are now in place to increase the
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density of the Groningen seismic monitoring network to an array of some 60 stations complemented by two
down-hole arrays of geophones located within and just above the reservoir. At each of these locations it is
planned to deploy borehole geophones to improve depth estimates and broadband accelerometers at the
surface to improve magnitude estimates and ground motion measurements.
The motivation for this densification is to significantly increase the precision of earthquake locations and to
increase the sensitivity of earthquake detection with the aim to identify all earthquakes of at least magnitude
0.5 within the Groningen Field. These enhancements should enable better characterisation of seismically
active regions and improve statistical constraints on the seismological model. This enhanced monitoring
network is expected to reveal if earthquakes initiate within, above, or below the reservoir and provides
opportunities to identify any localised seismicity on mapped faults.
Additional opportunities for improved acquisition of complementary seismic and geodetic data and the
application of recent developments in waveform-based earthquake data processing methods are also
under development.

13.6

References

Kamp, H.G.J. Brief van De Minister Van Economische Zaken. Kamerstukken (Tweede Kamer der StatenGeneraal, www.overheid.nl), Vergaderjaar 2012–2013, Kamerstuk 33529, nr. 2.
Muntendam-Bos, A. G., & Waal, J. A. de. (2013). Reassessment of the probability of higher magnitude
earthquakes in the Groningen gas field: Including a position statement by KNMI. Retrieved from
http://www.sodm.nl/sites/default/files/redactie/20130116 Groningen seismicity report final.pdf

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